The simple geometry of transmission and stabilization in closed and open economies

This paper provides an introduction to the recent literature on macroeconomic stabilization in closed and open economies. We present a stylized theoretical framework, and illustrate its main properties with the help of an intuitive graphical apparatus. Among the issues we discuss: optimal monetary policy and the welfare gains from macroeconomic stabilization; international transmission of real and monetary shocks and the role of exchange rate pass-through; the design of optimal exchange rate regimes and monetary coordination among interdependent economies.Classification-JEL: E31, E52, F42optimal monetary policy, nominal rigidities, exchange rate pass-through, international cooperation

The Simple Geometry of Transmission and Stabilization in Closed and Open Economies

This paper provides an introduction to the recent literature on macroeconomic stabilization in closed and open economies. We present a stylized theoretical framework, and illustrate its main properties with the help of an intuitive graphical apparatus. Among the issues we discuss: optimal monetary policy and the welfare gains from macroeconomic stabilization; international transmission of real and monetary shocks and the role of exchange rate pass-through; the design of optimal exchange rate regimes and monetary coordination among interdependent economies.

A Targeting Approach To Disturbance Rejection In Multi-Agent Systems

This thesis focuses on deadbeat disturbance rejection for discrete-time linear multi-agent systems. The multi-agent systems, on which Spieser and Shams’ decentralized deadbeat output regulation problem is based, are extended by including disturbance agents. Specifically, we assume that there are one or more disturbance agents interacting with the plant agents in some known manner. The disturbance signals are assumed to be unmeasured and, for simplicity, constant. Control agents are introduced to interact with the plant agents, and each control agent is assigned a target plant agent. The goal is to drive the outputs of all plant agents to zero in finite time, despite the presence of the disturbances. In the decentralized deadbeat output regulation problem, two analysis schemes were introduced: targeting analysis, which is used to determine whether or not control laws can be found to regulate, not all the agents, but only the target agents; and growing analysis, which is used to determine the behaviour of all the non-target agents when the control laws are applied. In this thesis these two analyses are adopted to the deadbeat disturbance rejection problem. A new necessary condition for successful disturbance rejection is derived, namely that a control agent must be connected to the same plant agent to which a disturbance agent is connected. This result puts a bound on the minimum number of control agents and constraints the locations of control agents. Then, given the premise that both targeting and growing analyses succeed in the special case where the disturbances are all ignored, a new control approach is proposed for the linear case based on the idea of integral control and the regulation methods of Spieser and Shams. Preliminary studies show that this approach is also suitable for some nonlinear systems

Computational Methods for Cognitive and Cooperative Robotics

In the last decades design methods in control engineering made substantial progress in the areas of robotics and computer animation. Nowadays these methods incorporate the newest developments in machine learning and artificial intelligence. But the problems of flexible and online-adaptive combinations of motor behaviors remain challenging for human-like animations and for humanoid robotics. In this context, biologically-motivated methods for the analysis and re-synthesis of human motor programs provide new insights in and models for the anticipatory motion synthesis. This thesis presents the author’s achievements in the areas of cognitive and developmental robotics, cooperative and humanoid robotics and intelligent and machine learning methods in computer graphics. The first part of the thesis in the chapter “Goal-directed Imitation for Robots” considers imitation learning in cognitive and developmental robotics. The work presented here details the author’s progress in the development of hierarchical motion recognition and planning inspired by recent discoveries of the functions of mirror-neuron cortical circuits in primates. The overall architecture is capable of ‘learning for imitation’ and ‘learning by imitation’. The complete system includes a low-level real-time capable path planning subsystem for obstacle avoidance during arm reaching. The learning-based path planning subsystem is universal for all types of anthropomorphic robot arms, and is capable of knowledge transfer at the level of individual motor acts. Next, the problems of learning and synthesis of motor synergies, the spatial and spatio-temporal combinations of motor features in sequential multi-action behavior, and the problems of task-related action transitions are considered in the second part of the thesis “Kinematic Motion Synthesis for Computer Graphics and Robotics”. In this part, a new approach of modeling complex full-body human actions by mixtures of time-shift invariant motor primitives in presented. The online-capable full-body motion generation architecture based on dynamic movement primitives driving the time-shift invariant motor synergies was implemented as an online-reactive adaptive motion synthesis for computer graphics and robotics applications. The last chapter of the thesis entitled “Contraction Theory and Self-organized Scenarios in Computer Graphics and Robotics” is dedicated to optimal control strategies in multi-agent scenarios of large crowds of agents expressing highly nonlinear behaviors. This last part presents new mathematical tools for stability analysis and synthesis of multi-agent cooperative scenarios.In den letzten Jahrzehnten hat die Forschung in den Bereichen der Steuerung und Regelung komplexer Systeme erhebliche Fortschritte gemacht, insbesondere in den Bereichen Robotik und Computeranimation. Die Entwicklung solcher Systeme verwendet heutzutage neueste Methoden und Entwicklungen im Bereich des maschinellen Lernens und der künstlichen Intelligenz. Die flexible und echtzeitfähige Kombination von motorischen Verhaltensweisen ist eine wesentliche Herausforderung für die Generierung menschenähnlicher Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme neue Erkenntnisse und Modelle für die antizipatorische Bewegungssynthese. Diese Dissertation präsentiert die Ergebnisse der Arbeiten des Autors im Gebiet der kognitiven und Entwicklungsrobotik, kooperativer und humanoider Robotersysteme sowie intelligenter und maschineller Lernmethoden in der Computergrafik. Der erste Teil der Dissertation im Kapitel “Zielgerichtete Nachahmung für Roboter” behandelt das Imitationslernen in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben neue Methoden für die hierarchische Bewegungserkennung und -planung, die durch Erkenntnisse zur Funktion der kortikalen Spiegelneuronen-Schaltkreise bei Primaten inspiriert wurden. Die entwickelte Architektur ist in der Lage, ‘durch Imitation zu lernen’ und ‘zu lernen zu imitieren’. Das komplette entwickelte System enthält ein echtzeitfähiges Pfadplanungssubsystem zur Hindernisvermeidung während der Durchführung von Armbewegungen. Das lernbasierte Pfadplanungssubsystem ist universell und für alle Arten von anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer Handlungen zu übertragen. Im zweiten Teil der Arbeit “Kinematische Bewegungssynthese für Computergrafik und Robotik” werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h. von räumlichen und räumlich-zeitlichen Kombinationen motorischer Bewegungselemente bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs übergängen behandelt. Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem wurde ein online-fähiger Synthesealgorithmus für Ganzköperbewegungen entwickelt, der auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde für verschiedene Probleme der Bewegungssynthese für die Computergrafik- und Roboteranwendungen implementiert. Das letzte Kapitel der Dissertation mit dem Titel “Kontraktionstheorie und selbstorganisierte Szenarien in der Computergrafik und Robotik” widmet sich optimalen Kontrollstrategien in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare Kinematik gekennzeichnet sind. Dieser letzte Teil präsentiert neue mathematische Werkzeuge für die Stabilitätsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien

The Role of Storage and Information in Stabilizing Food Prices and Supplies

High and volatile food prices can push people into poverty, impact production and consumption, discourage investments, and lead to social unrest. Thus, due to occasional global food shortages as in 2007/08 and frequent regional shortages, many governments apply price stabilization policies. However, academic and political controversies about appropriate measures persist. This thesis explores the role of private and public storage, information, trade policies, international cooperation, and price-responsive production in stabilizing food prices. In spite of its relevance for resource allocation, knowledge of the quality of global supply and demand estimates is limited. Hence, USDA, IGC, and FAO-AMIS estimates are compared using cointegration analysis, granger causality tests and three other methods. Estimation differences are found to be driven by methodological discrepancies rather than differing information. Differences are large for stocks and trade and persist over time revealing a comovement of the estimates. Averaging over sources can improve robustness and precision. Despite its importance for the WTO and other trade agreements, knowledge of stabilization policies in an open economy is scarce. Additionally, private storage has been neglected in previous studies on international cooperation. Thus, emergency reserves, subsidized private storage, and strategic trade restrictions are compared in terms of their costs and impacts on price levels, volatility, and extreme events. A rational expectation partial equilibrium model is applied to capture dynamic interactions between agents. Private storage is found to be effective in reducing price volatility, whereas, complementarily, reserves are more effective in preventing extreme events. While free trade is usually beneficial, incentives for restricting exports may arise if stabilization policies are not aligned or the production variability differs too much. Asymmetric policies can explain large price increases as observed in 2007/08. With some adjustments, the above model is used to present a new empirical validation method for the competitive storage model, the workhorse in numerical analysis of private storage. For the first time, the validation uses actual stock data. By applying a surface response methodology, this study derives a reduced-form equation which is shown to perform well as a surrogate model for private storage in theory and empirical tests. This allows directly quantifying stock determinants and facilitates high-dimensional modelling exercises. As an empirical case study, India’s public stockholding program, which suffers from surging stocks and costs, is analyzed. Necessary reforms require understanding how policies impact stocks, which is quantified for the first time. Thus, expected policy impacts on public rice stocks are deduced from economic theory and tested empirically. Private stock determinants are quantified by combining the reduced-form storage equation with an instrumental variable approach. Public storage is found to be inert, lacking crisis-responsive consumer protection and driven by the minimum support price, market supply, and export bans. The 29% increase of the real support price in 2008 contributed 4.9 million tons to public stocks, the export ban another 2.9. Each ton of public stocks crowds out half a ton of private stocks; however, speculative storage activities persist. Policy makers seem to be unaware of the problematic policy interactions. China’s demand growth and reluctance to rely on imports for its main food crops underline the need for a responsive supply, i.e. farmers making use of the latest price information. Hence, the time-dependent price responsiveness of supply is analyzed using the difference GMM estimator on province panel data. Production responds most to prices around planting time indicating the up-to-dateness of farmers’ price information. High temperatures reduce production thereby highlighting the importance of limiting climate change and adapting to it.Die Rolle von Lagerhaltung und Informationen bei der Stabilisierung von Angebot und Preisen von Nahrungsmitteln Hohe und volatile Nahrungsmittelpreise können Menschen in Armut drängen, Produktion, Konsum sowie Investitionen beeinflussen und Unruhen auslösen. Wegen gelegentlicher globaler Nahrungsmittelengpässe wie in 2007/08 und häufiger regionaler Engpässe verwenden viele Regierungen Preisstabilisierungspolitiken. Diese führen jedoch nach wie vor zu akademischen und politischen Kontroversen. Diese Arbeit erforscht die Rolle staatlicher und privater Lagerhaltung, von Informationen, Handelspolitiken, internationaler Kooperation, sowie einer auf Preisänderungen reagierenden Produktion bei der Preisstabilisierung. Das Wissen über die Qualität globaler Angebots- und Nachfrageschätzungen ist trotz seiner Bedeutung für die Ressourcenallokation begrenzt. Daher werden Schätzungen des USDA, IGC, und FAO-AMIS mit Hilfe von Kointegrationstests, Granger-Kausalitätsstests und weiteren Methoden verglichen. Unterschiede zwischen Schätzungen bestehen über die Zeit fort, sind groß für Lager- und Handelsdaten und scheinen eher das Resultat verschiedener Methoden als abweichender Informationen zu sein. Eine Mittelung über die Quellen kann Präzision und Robustheit verbessern. Trotz der Wichtigkeit für die WTO und andere Handelsabkommen ist das Wissen über Stabilisierungspolitiken in offenen Volkswirtschaften gering und frühere Studien zur Kooperation haben private Lager ignoriert. Daher wird die theoretische Kosteneffizienz der Stabilisierung durch Notfallreserven, subventionierter privater Lagerhaltung und strategischer Handelspolitiken verglichen. Ein partielles Gleichgewichtsmodel mit rationalen Erwartungen erfasst die dynamischen Interaktionen der Akteure. Private Lager sind effektiv in der Volatilitätsverringerung während, komplementär dazu, eine Notreserve effektiver in der Verhinderung extremer Preise ist. Freier Handel ist meistens vorteilhaft, aber Anreize für Exportrestriktionen entstehen bei asymmetrischen Stabilisierungspolitiken oder Produktionsvariabilitäten. Asymmetrische Politiken können auch starke Preisanstiege erklären, wie sie beispielsweise in 2007/08 beobachtet wurden. Modifiziert ermöglicht das obige Model eine neue empirische Validierungsmethode für das kompetitive Lagerhaltungsmodel, das Standardmodel privater Lagerhaltung. Erstmals werden bei der Validierung Lagerbestände berücksichtigt. Mit Hilfe der Antwortflächenmethode wird eine reduzierte Gleichung hergeleitet, die ein genaues Ersatzmodel in der Theorie wie auch bei empirischen Tests darstellt. Dies erlaubt die direkte Quantifizierung von Lagerhaltungsdeterminanten und ermöglicht hoch-dimensionale Modellbildung mit privater Lagerhaltung. Steigende Lagerbestände und Kosten von Indiens staatlichem Lagerhaltungsprogramm erfordern Reformen. Diese bedürfen Kenntnisse darüber, wie Regulierungen Lager beeinflussen, was erstmals in dieser Studie quantifiziert wird. Der Einfluss von Richtlinien auf Reisbestände wird aus ökonomischer Theorie hergeleitet und empirisch getestet. Determinanten privater Reisbestände werden mit der reduzierten Gleichung und einem Instrumentalvariablenansatz quantifiziert. Öffentliche Lager erscheinen träge, beeinflusst von Angebot, Exportverboten und Minimum Support Price (MSP) und entbehren eines krisenabhängigen Konsumentenschutzes. Der 29%ige Anstieg des realen MSP in 2008 führte zu 4,9, das Exportverbot zu 2,9 Millionen Tonnen mehr in öffentlichen Lagern. Jede öffentlich gelagerte Tonne verdrängt eine halbe private, aber spekulative Lagerhaltung besteht fort. Die problematischen Interaktionen von Maßnahmen scheinen unbekannt. Chinas Nachfrageanstieg und Abneigung gegen Importabhängigkeit für Grundnahrungsmittel erfordern eine schnelle Reaktion der Produktion auf Preisänderungen. Daher wird die zeitabhängige Preisantwort der Produktion mithilfe des difference GMM Schätzers und Provinz-Paneldaten untersucht. Preise zur Anbauzeit stellen sich als am Wichtigsten heraus, was die Aktualität der Preisinformationen der Landwirte bezeugt. Hohe Temperaturen verringern die Produktion und unterstreichen so die Notwendigkeit den Klimawandel zu begrenzen und sich anzupassen

An Emergent Architecture for Scaling Decentralized Communication Systems (DCS)

With recent technological advancements now accelerating the mobile and wireless Internet solution space, a ubiquitous computing Internet is well within the research and industrial community's design reach - a decentralized system design, which is not solely driven by static physical models and sound engineering principals, but more dynamically, perhaps sub-optimally at initial deployment and socially-influenced in its evolution. To complement today's Internet system, this thesis proposes a Decentralized Communication System (DCS) architecture with the following characteristics: flat physical topologies with numerous compute oriented and communication intensive nodes in the network with many of these nodes operating in multiple functional roles; self-organizing virtual structures formed through alternative mobility scenarios and capable of serving ad hoc networking formations; emergent operations and control with limited dependency on centralized control and management administration. Today, decentralized systems are not commercially scalable or viable for broad adoption in the same way we have to come to rely on the Internet or telephony systems. The premise in this thesis is that DCS can reach high levels of resilience, usefulness, scale that the industry has come to experience with traditional centralized systems by exploiting the following properties: (i.) network density and topological diversity; (ii.) self-organization and emergent attributes; (iii.) cooperative and dynamic infrastructure; and (iv.) node role diversity. This thesis delivers key contributions towards advancing the current state of the art in decentralized systems. First, we present the vision and a conceptual framework for DCS. Second, the thesis demonstrates that such a framework and concept architecture is feasible by prototyping a DCS platform that exhibits the above properties or minimally, demonstrates that these properties are feasible through prototyped network services. Third, this work expands on an alternative approach to network clustering using hierarchical virtual clusters (HVC) to facilitate self-organizing network structures. With increasing network complexity, decentralized systems can generally lead to unreliable and irregular service quality, especially given unpredictable node mobility and traffic dynamics. The HVC framework is an architectural strategy to address organizational disorder associated with traditional decentralized systems. The proposed HVC architecture along with the associated promotional methodology organizes distributed control and management services by leveraging alternative organizational models (e.g., peer-to-peer (P2P), centralized or tiered) in hierarchical and virtual fashion. Through simulation and analytical modeling, we demonstrate HVC efficiencies in DCS structural scalability and resilience by comparing static and dynamic HVC node configurations against traditional physical configurations based on P2P, centralized or tiered structures. Next, an emergent management architecture for DCS exploiting HVC for self-organization, introduces emergence as an operational approach to scaling DCS services for state management and policy control. In this thesis, emergence scales in hierarchical fashion using virtual clustering to create multiple tiers of local and global separation for aggregation, distribution and network control. Emergence is an architectural objective, which HVC introduces into the proposed self-management design for scaling and stability purposes. Since HVC expands the clustering model hierarchically and virtually, a clusterhead (CH) node, positioned as a proxy for a specific cluster or grouped DCS nodes, can also operate in a micro-capacity as a peer member of an organized cluster in a higher tier. As the HVC promotional process continues through the hierarchy, each tier of the hierarchy exhibits emergent behavior. With HVC as the self-organizing structural framework, a multi-tiered, emergent architecture enables the decentralized management strategy to improve scaling objectives that traditionally challenge decentralized systems. The HVC organizational concept and the emergence properties align with and the view of the human brain's neocortex layering structure of sensory storage, prediction and intelligence. It is the position in this thesis, that for DCS to scale and maintain broad stability, network control and management must strive towards an emergent or natural approach. While today's models for network control and management have proven to lack scalability and responsiveness based on pure centralized models, it is unlikely that singular organizational models can withstand the operational complexities associated with DCS. In this work, we integrate emergence and learning-based methods in a cooperative computing manner towards realizing DCS self-management. However, unlike many existing work in these areas which break down with increased network complexity and dynamics, the proposed HVC framework is utilized to offset these issues through effective separation, aggregation and asynchronous processing of both distributed state and policy. Using modeling techniques, we demonstrate that such architecture is feasible and can improve the operational robustness of DCS. The modeling emphasis focuses on demonstrating the operational advantages of an HVC-based organizational strategy for emergent management services (i.e., reachability, availability or performance). By integrating the two approaches, the DCS architecture forms a scalable system to address the challenges associated with traditional decentralized systems. The hypothesis is that the emergent management system architecture will improve the operational scaling properties of DCS-based applications and services. Additionally, we demonstrate structural flexibility of HVC as an underlying service infrastructure to build and deploy DCS applications and layered services. The modeling results demonstrate that an HVC-based emergent management and control system operationally outperforms traditional structural organizational models. In summary, this thesis brings together the above contributions towards delivering a scalable, decentralized system for Internet mobile computing and communications

Large scale stochastic control: Algorithms, optimality and stability

Optimal control of large-scale multi-agent networked systems which describe social networks, macro-economies, traffic and robot swarms is a topic of interest in engineering, biophysics and economics. A central issue is constructing scalable control-theoretic frameworks when the number of agents is infinite. In this work, we exploit PDE representations of the optimality laws in order to provide a tractable approach to ensemble (open loop) and closed loop control of such systems. A centralized open loop optimal control problem of an ensemble of agents driven by jump noise is solved by a sampling algorithm based on the infinite dimensional minimum principle to solve it. The relationship between the infinite dimensional minimum principle and dynamic programming principles is established for this problem. Mean field game (MFG) models expressed as PDE systems are used to describe emergent phenomenon in decentralized feedback optimal control models of a continuum of interacting agents with stochastic dynamics. However, stability analysis of MFG models remains a challenging problem, since they exhibit non-unique solutions in the absence of a monotonicity assumption on the cost function. This thesis addresses the key issue of stability and control design in MFGs. Specifically, we present detailed results on a models for flocking and population evolution. An interesting connection between MFG models and the imaginary-time Schr¨odinger equation is used to obtain explicit stability constraints on the control design in the case of non-interacting agents. Compared to prior works on this topic which apply only to agents obeying very simple integrator dynamics, we treat nonlinear agent dynamics and also provide analytical design constraints.Ph.D

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Manipulation Planning for Forceful Human-Robot-Collaboration

This thesis addresses the problem of manipulation planning for forceful human-robot collaboration. Particularly, the focus is on the scenario where a human applies a sequence of changing external forces through forceful operations (e.g. cutting a circular piece off a board) on an object that is grasped by a cooperative robot. We present a range of planners that 1) enable the robot to stabilize and position the object under the human applied forces by exploiting supports from both the object-robot and object-environment contacts; 2) improve task efficiency by minimizing the need of configuration and grasp changes required by the changing external forces; 3) improve human comfort during the forceful interaction by optimizing the defined comfort criteria. We first focus on the instance of using only robotic grasps, where the robot is supposed to grasp/regrasp the object multiple times to keep it stable under the changing external forces. We introduce a planner that can generate an efficient manipulation plan by intelligently deciding when the robot should change its grasp on the object as the human applies the forces, and choosing subsequent grasps such that they minimize the number of regrasps required in the long-term. The planner searches for such an efficient plan by first finding a minimal sequence of grasp configurations that are able to keep the object stable under the changing forces, and then generating connecting trajectories to switch between the planned configurations, i.e. planning regrasps. We perform the search for such a grasp (configuration) sequence by sampling stable configurations for the external forces, building an operation graph using these stable configurations and then searching the operation graph to minimize the number of regrasps. We solve the problem of bimanual regrasp planning under the assumption of no support surface, enabling the robot to regrasp an object in the air by finding intermediate configurations at which both the bimanual and unimanual grasps can hold the object stable under gravity. We present a variety of experiments to show the performance of our planner, particularly in minimizing the number of regrasps for forceful manipulation tasks and planning stable regrasps. We then explore the problem of using both the object-environment contacts and object-robot contacts, which enlarges the set of stable configurations and thus boosts the robot’s capability in stabilizing the object under external forces. We present a planner that can intelligently exploit the environment’s and robot’s stabilization capabilities within a unified planning framework to search for a minimal number of stable contact configurations. A big computational bottleneck in this planner is due to the static stability analysis of a large number of candidate configurations. We introduce a containment relation between different contact configurations, to efficiently prune the stability checking process. We present a set of real-robot and simulated experiments illustrating the effectiveness of the proposed framework. We present a detailed analysis of the proposed containment relationship, particularly in improving the planning efficiency. We present a planning algorithm to further improve the cooperative robot behaviour concerning human comfort during the forceful human-robot interaction. Particularly, we are interested in empowering the robot with the capability of grasping and positioning the object not only to ensure the object stability against the human applied forces, but also to improve human experience and comfort during the interaction. We address human comfort as the muscular activation level required to apply a desired external force, together with the human spatial perception, i.e. the so-called peripersonal-space comfort during the interaction. We propose to maximize both comfort metrics to optimize the robot and object configuration such that the human can apply a forceful operation comfortably. We present a set of human-robot drilling and cutting experiments which verify the efficiency of the proposed metrics in improving the overall comfort and HRI experience, without compromising the force stability. In addition to the above planning work, we present a conic formulation to approximate the distribution of a forceful operation in the wrench space with a polyhedral cone, which enables the planner to efficiently assess the stability of a system configuration even in the presence of force uncertainties that are inherent in the human applied forceful operations. We also develop a graphical user interface, which human users can easily use to specify various forceful tasks, i.e. sequences of forceful operations on selected objects, in an interactive manner. The user interface ties in human task specification, on-demand manipulation planning and robot-assisted fabrication together. We present a set of human-robot experiments using the interface demonstrating the feasibility of our system. In short, in this thesis we present a series of planners for object manipulation under changing external forces. We show the object contacts with the robot and the environment enable the robot to manipulate an object under external forces, while making the most of the object contacts has the potential to eliminate redundant changes during manipulation, e.g. regrasp, and thus improve task efficiency and smoothness. We also show the necessity of optimizing human comfort in planning for forceful human-robot manipulation tasks. We believe the work presented here can be a key component in a human-robot collaboration framework