634 research outputs found
Motion Imitation Based on Sparsely Sampled Correspondence
Existing techniques for motion imitation often suffer a certain level of
latency due to their computational overhead or a large set of correspondence
samples to search. To achieve real-time imitation with small latency, we
present a framework in this paper to reconstruct motion on humanoids based on
sparsely sampled correspondence. The imitation problem is formulated as finding
the projection of a point from the configuration space of a human's poses into
the configuration space of a humanoid. An optimal projection is defined as the
one that minimizes a back-projected deviation among a group of candidates,
which can be determined in a very efficient way. Benefited from this
formulation, effective projections can be obtained by using sparse
correspondence. Methods for generating these sparse correspondence samples have
also been introduced. Our method is evaluated by applying the human's motion
captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be
realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor
Temporal Relational Reasoning in Videos
Temporal relational reasoning, the ability to link meaningful transformations
of objects or entities over time, is a fundamental property of intelligent
species. In this paper, we introduce an effective and interpretable network
module, the Temporal Relation Network (TRN), designed to learn and reason about
temporal dependencies between video frames at multiple time scales. We evaluate
TRN-equipped networks on activity recognition tasks using three recent video
datasets - Something-Something, Jester, and Charades - which fundamentally
depend on temporal relational reasoning. Our results demonstrate that the
proposed TRN gives convolutional neural networks a remarkable capacity to
discover temporal relations in videos. Through only sparsely sampled video
frames, TRN-equipped networks can accurately predict human-object interactions
in the Something-Something dataset and identify various human gestures on the
Jester dataset with very competitive performance. TRN-equipped networks also
outperform two-stream networks and 3D convolution networks in recognizing daily
activities in the Charades dataset. Further analyses show that the models learn
intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1
Thinking the GOAT: imitating tennis styles
A tactically aware coach is key to improving tennis playersâ games; a coach analyses past matches with two considerations in mind: 1) the style of the player and how that style translates to real-world shot-making, and 2) the intent of a shot, irrespective of the outcome. Modern Hawk-Eye technology deployed in top-tier tournaments has enabled deeper analysis of professional matches than ever before. The aim of this paper is to emulate and augment the qualities of great coaches using data collected by Hawk-Eye; we develop a deep learning approach to imitate tennis playersâ responses, to learn individual player styles efficiently, and we demonstrate this using performance metrics and illustrations
Massively Scalable Inverse Reinforcement Learning in Google Maps
Optimizing for humans' latent preferences is a grand challenge in route
recommendation, where globally-scalable solutions remain an open problem.
Although past work created increasingly general solutions for the application
of inverse reinforcement learning (IRL), these have not been successfully
scaled to world-sized MDPs, large datasets, and highly parameterized models;
respectively hundreds of millions of states, trajectories, and parameters. In
this work, we surpass previous limitations through a series of advancements
focused on graph compression, parallelization, and problem initialization based
on dominant eigenvectors. We introduce Receding Horizon Inverse Planning
(RHIP), which generalizes existing work and enables control of key performance
trade-offs via its planning horizon. Our policy achieves a 16-24% improvement
in global route quality, and, to our knowledge, represents the largest instance
of IRL in a real-world setting to date. Our results show critical benefits to
more sustainable modes of transportation (e.g. two-wheelers), where factors
beyond journey time (e.g. route safety) play a substantial role. We conclude
with ablations of key components, negative results on state-of-the-art
eigenvalue solvers, and identify future opportunities to improve scalability
via IRL-specific batching strategies
Optimal foraging strategies can be learned
The foraging behavior of animals is a paradigm of target search in nature.
Understanding which foraging strategies are optimal and how animals learn them
are central challenges in modeling animal foraging. While the question of
optimality has wide-ranging implications across fields such as economy,
physics, and ecology, the question of learnability is a topic of ongoing debate
in evolutionary biology. Recognizing the interconnected nature of these
challenges, this work addresses them simultaneously by exploring optimal
foraging strategies through a reinforcement learning framework. To this end, we
model foragers as learning agents. We first prove theoretically that maximizing
rewards in our reinforcement learning model is equivalent to optimizing
foraging efficiency. We then show with numerical experiments that, in the
paradigmatic model of non-destructive search, our agents learn foraging
strategies which outperform the efficiency of some of the best known strategies
such as L\'evy walks. These findings highlight the potential of reinforcement
learning as a versatile framework not only for optimizing search strategies but
also to model the learning process, thus shedding light on the role of learning
in natural optimization processes.Comment: 12 pages (6 main text) and 9 figures. Codes and tutorials available
at: https://gorkamunoz.github.io/rl_opts
Reasoning About Liquids via Closed-Loop Simulation
Simulators are powerful tools for reasoning about a robot's interactions with
its environment. However, when simulations diverge from reality, that reasoning
becomes less useful. In this paper, we show how to close the loop between
liquid simulation and real-time perception. We use observations of liquids to
correct errors when tracking the liquid's state in a simulator. Our results
show that closed-loop simulation is an effective way to prevent large
divergence between the simulated and real liquid states. As a direct
consequence of this, our method can enable reasoning about liquids that would
otherwise be infeasible due to large divergences, such as reasoning about
occluded liquid.Comment: Robotics: Science & Systems (RSS), July 12-16, 2017. Cambridge, MA,
US
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 authorâs 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 âGoal-directed Imitation for Robotsâ considers imitation learning in cognitive and developmental robotics.
The work presented here details the authorâs 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 âlearning for
imitationâ and âlearning by imitationâ. 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
âKinematic Motion Synthesis for Computer Graphics and Roboticsâ. 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 âContraction Theory and Self-organized Scenarios
in Computer Graphics and Roboticsâ 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
kĂŒnstlichen Intelligenz. Die flexible und echtzeitfĂ€hige Kombination von motorischen Verhaltensweisen
ist eine wesentliche Herausforderung fĂŒr die Generierung menschenĂ€hnlicher
Animationen und in der humanoiden Robotik. In diesem Zusammenhang liefern biologisch
motivierte Methoden zur Analyse und Resynthese menschlicher motorischer Programme
neue Erkenntnisse und Modelle fĂŒr die antizipatorische Bewegungssynthese.
Diese Dissertation prÀsentiert 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 âZielgerichtete Nachahmung fĂŒr Roboterâ behandelt das Imitationslernen
in der kognitiven und Entwicklungsrobotik. Die vorgestellten Arbeiten beschreiben
neue Methoden fĂŒr 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, âdurch Imitation zu lernenâ
und âzu lernen zu imitierenâ. Das komplette entwickelte System enthĂ€lt ein echtzeitfĂ€higes
Pfadplanungssubsystem zur Hindernisvermeidung wĂ€hrend der DurchfĂŒhrung von Armbewegungen.
Das lernbasierte Pfadplanungssubsystem ist universell und fĂŒr alle Arten von
anthropomorphen Roboterarmen in der Lage, Wissen auf der Ebene einzelner motorischer
Handlungen zu ĂŒbertragen.
Im zweiten Teil der Arbeit âKinematische Bewegungssynthese fĂŒr Computergrafik und
Robotikâ werden die Probleme des Lernens und der Synthese motorischer Synergien, d.h.
von rÀumlichen und rÀumlich-zeitlichen Kombinationen motorischer Bewegungselemente
bei Bewegungssequenzen und bei aufgabenbezogenen Handlungs ĂŒbergĂ€ngen behandelt.
Es wird ein neuer Ansatz zur Modellierung komplexer menschlicher Ganzkörperaktionen
durch Mischungen von zeitverschiebungsinvarianten Motorprimitiven vorgestellt. Zudem
wurde ein online-fĂ€higer Synthesealgorithmus fĂŒr Ganzköperbewegungen entwickelt, der
auf dynamischen Bewegungsprimitiven basiert, die wiederum auf der Basis der gelernten
verschiebungsinvarianten Primitive konstruiert werden. Dieser Algorithmus wurde fĂŒr
verschiedene Probleme der Bewegungssynthese fĂŒr die Computergrafik- und Roboteranwendungen
implementiert.
Das letzte Kapitel der Dissertation mit dem Titel âKontraktionstheorie und selbstorganisierte
Szenarien in der Computergrafik und Robotikâ widmet sich optimalen Kontrollstrategien
in Multi-Agenten-Szenarien, wobei die Agenten durch eine hochgradig nichtlineare
Kinematik gekennzeichnet sind. Dieser letzte Teil prÀsentiert neue mathematische Werkzeuge
fĂŒr die StabilitĂ€tsanalyse und Synthese von kooperativen Multi-Agenten-Szenarien
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