7 research outputs found

    Dyadic collaborative manipulation formalism for optimizing human-robot teaming

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    Dyadic collaborative Manipulation (DcM) is a term we use to refer to a team of two individuals, the agent and the partner, jointly manipulating an object. The two individuals partner together to form a distributed system, augmenting their manipulation abilities. Effective collaboration between the two individuals during joint action depends on: (i) the breadth of the agent’s action repertoire, (ii) the level of model acquaintance between the two individuals, (iii) the ability to adapt online of one’s own actions to the actions of their partner, and (iv) the ability to estimate the partner’s intentions and goals. Key to the successful completion of co-manipulation tasks with changing goals is the agent’s ability to change grasp-holds, especially in large object co-manipulation scenarios. Hence, in this work we developed a Trajectory Optimization (TO) method to enhance the repertoire of actions of robotic agents, by enabling them to plan and execute hybrid motions, i.e. motions that include discrete contact transitions, continuous trajectories and force profiles. The effectiveness of the TO method is investigated numerically and in simulation, in a number of manipulation scenarios with both a single and a bimanual robot. In addition, it is worth noting that transitions from free motion to contact is a challenging problem in robotics, in part due to its hybrid nature. Additionally, disregarding the effects of impacts at the motion planning level often results in intractable impulsive contact forces. To address this challenge, we introduce an impact-aware multi-mode TO method that combines hybrid dynamics and hybrid control in a coherent fashion. A key concept in our approach is the incorporation of an explicit contact force transmission model into the TO method. This allows the simultaneous optimization of the contact forces, contact timings, continuous motion trajectories and compliance, while satisfying task constraints. To demonstrate the benefits of our method, we compared our method against standard compliance control and an impact-agnostic TO method in physical simulations. Also, we experimentally validated the proposed method with a robot manipulator on the task of halting a large-momentum object. Further, we propose a principled formalism to address the joint planning problem in DcM scenarios and we solve the joint problem holistically via model-based optimization by representing the human's behavior as task space forces. The task of finding the partner-aware contact points, forces and the respective timing of grasp-hold changes are carried out by a TO method using non-linear programming. Using simulations, the capability of the optimization method is investigated in terms of robot policy changes (trajectories, timings, grasp-holds) to potential changes of the collaborative partner policies. We also realized, in hardware, effective co-manipulation of a large object by the human and the robot, including eminent grasp changes as well as optimal dyadic interactions to realize the joint task. To address the online adaptation challenge of joint motion plans in dyads, we propose an efficient bilevel formulation which combines graph search methods with trajectory optimization, enabling robotic agents to adapt their policy on-the-fly in accordance to changes of the dyadic task. This method is the first to empower agents with the ability to plan online in hybrid spaces; optimizing over discrete contact locations, contact sequence patterns, continuous trajectories, and force profiles for co-manipulation tasks. This is particularly important in large object co-manipulation tasks that require on-the-fly plan adaptation. We demonstrate in simulation and with robot experiments the efficacy of the bilevel optimization by investigating the effect of robot policy changes in response to real-time alterations of the goal. This thesis provides insight into joint manipulation setups performed by human-robot teams. In particular, it studies computational models of joint action and exploits the uncharted hybrid action space, that is especially relevant in general manipulation and co-manipulation tasks. It contributes towards developing a framework for DcM, capable of planning motions in the contact-force space, realizing these motions while considering impacts and joint action relations, as well as adapting on-the-fly these motion plans with respect to changes of the co-manipulation goals

    Experience-driven optimal motion synthesis in complex and shared environments

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    Optimal loco-manipulation planning and control for high-dimensional systems based on general, non-linear optimisation allows for the specification of versatile motion subject to complex constraints. However, complex, non-linear system and environment dynamics, switching contacts, and collision avoidance in cluttered environments introduce non-convexity and discontinuity in the optimisation space. This renders finding optimal solutions in complex and changing environments an open and challenging problem in robotics. Global optimisation methods can take a prohibitively long time to converge. Slow convergence makes them unsuitable for live deployment and online re-planning of motion policies in response to changes in the task or environment. Local optimisation techniques, in contrast, converge fast within the basin of attraction of a minimum but may not converge at all without a good initial guess as they can easily get stuck in local minima. Local methods are, therefore, a suitable choice provided we can supply a good initial guess. If a similarity between problems can be found and exploited, a memory of optimal solutions can be computed and compressed efficiently in an offline computation process. During runtime, we can query this memory to bootstrap motion synthesis by providing a good initial seed to the local optimisation solver. In order to realise such a system, we need to address several connected problems and questions: First, the formulation of the optimisation problem (and its parametrisation to allow solutions to transfer to new scenarios), and related, the type and granularity of user input, along with a strategy for recovery and feedback in case of unexpected changes or failure. Second, a sampling strategy during the database/memory generation that explores the parameter space efficiently without resorting to exhaustive measures---i.e., to balance storage size/memory with online runtime to adapt/repair the initial guess. Third, the question of how to represent the problem and environment to parametrise, compute, store, retrieve, and exploit the memory efficiently during pre-computation and runtime. One strategy to make the problem computationally tractable is to decompose planning into a series of sequential sub-problems, e.g., contact-before-motion approaches which sequentially perform goal state planning, contact planning, motion planning, and encoding. Here, subsequent stages operate within the null-space of the constraints of the prior problem, such as the contact mode or sequence. This doctoral thesis follows this line of work. It investigates general optimisation-based formulations for motion synthesis along with a strategy for exploration, encoding, and exploitation of a versatile memory-of-motion for providing an initial guess to optimisation solvers. In particular, we focus on manipulation in complex environments with high-dimensional robot systems such as humanoids and mobile manipulators. The first part of this thesis focuses on collision-free motion generation to reliably generate motions. We present a general, collision-free inverse kinematics method using a combination of gradient-based local optimisation with random/evolution strategy restarting to achieve high success rates and avoid local minima. We use formulations for discrete collision avoidance and introduce a novel, computationally fast continuous collision avoidance objective based on conservative advancement and harmonic potential fields. Using this, we can synthesise continuous-time collision-free motion plans in the presence of moving obstacles. It further enables to discretise trajectories with fewer waypoints, which in turn considerably reduces the optimisation problem complexity, and thus, time to solve. The second part focuses on problem representations and exploration. We first introduce an efficient solution encoding for trajectory library-based approaches. This representation, paired with an accompanying exploration strategy for offline pre-computation, permits the application of inexpensive distance metrics during runtime. We demonstrate how our method efficiently re-uses trajectory samples, increases planning success rates, and reduces planning time while being highly memory-efficient. We subsequently present a method to explore the topological features of the solution space using tools from computational homology. This enables us to cluster solutions according to their inherent structure which increases the success of warm-starting for problems with discontinuities and multi-modality. The third part focuses on real-world deployment in laboratory and field experiments as well as incorporating user input. We present a framework for robust shared autonomy with a focus on continuous scene monitoring for assured safety. This framework further supports interactive adjustment of autonomy levels from fully teleoperated to automatic execution of stored behaviour sequences. Finally, we present sensing and control for the integration and embodiment of the presented methodology in high-dimensional real-world platforms used in laboratory experiments and real-world deployment. We validate our presented methods using hardware experiments on a variety of robot platforms demonstrating generalisation to other robots and environments

    Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search

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    Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans that inspect the points of interest grows exponentially with the number of inspected points. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion planning roadmap using sampling-based algorithms, and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated planar 5DOF manipulator inspection task and on a medical endoscopic inspection task for a continuum parallel surgical robot in anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection paths orders of magnitudes faster than a prior state-of-the-art method.Comment: RSS 201

    Effective Footstep Planning for Humanoids Using Homotopy-Class Guidance

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    Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot’s feet—a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is using search-based planners such as A* and its many variants. To do so, these search-based planners have to be endowed with effective heuristics to efficiently guide them through the search space. However, designing effective heuristics is a time-consuming task that requires the user to have good domain knowledge. Thus, our goal is to be able to effectively plan the footstep motions taken by a humanoid robot while obviating the burden on the user to carefully design local-minima free heuristics. To this end, we propose to use user-defined homotopy classes in the workspace that are intuitive to define. These homotopy classes are used to automatically generate heuristic functions that efficiently guide the footstep planner. We compare our approach for footstep planning with a standard approach that uses a heuristic common to footstep planning. In simple scenarios, the performance of both algorithms is comparable. However, in more complex scenarios our approach allows for a speedup in planning of several orders of magnitude when compared to the standard approach

    Using Model-based Optimal Control for Conceptional Motion Generation for the Humannoid Robot HRP-2 14 and Design Investigations for Exo-Skeletons

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    The research field of bipedal locomotion has been active since a few decades now. At one hand, the legged locomotion principle comprises highly flexible and robust mobility for technical applications. At the other hand, a thorough technical understanding of bipedalism supports efforts of clinicians and engineers to help people, suffering from reduced locomotion capabilities caused by fatal incidents. Since the technology enabled the construction of numerous robotic devices, among them: various humanoids, researchers started to investigate bipedalism by abstraction and adoption for technical applications. Findings from humanoid robotics are further exploited for the construction of devices for human performance augmentation and mobility support or gait rehabilitation, among them: orthosis and exo-skeletons. Although this research continuously progresses, the motion capacities of humanoid robots still lack far behind those of humans in terms of forward velocity, robustness and appearance of the overall motion. Generally, it is claimed that the difference of performance between humans and robotics is not only due to the limiting characteristics of the employed technology, e.g. constructive lack of specific determinants of gait for bipedalism or dynamic limits of the actuation system, but as well to the adopted methods for motion generation and control. For humanoid robotics, methods for motion generation are classified into optimization-based methods and those that employ heuristics, that are mostly distinguished based on the problem complexity (computation time) and the resulting dynamic error between the generated motion and the dynamics of the real robot. The implementation of the dynamic motion on the robotic platform is usually comprised with an on-line stabilizing control system. This control system must then identify and resolve instantaneously the dynamic error to maintain a continuously stable operation of the device. A large dynamic error and breach of the dynamic limits of the actuation system can quickly lead to a fatal destabilization of the device. This work proposes a contribution to the model computation and the strategy of the problem formulation of direct multiple-shooting based optimal control (Bock et. al.) for dynamically stable optimization-based motion generation. The computation of the whole-body dynamic model inside the optimization relies either on forward or inverse dynamics approach. As the inverse dynamics approach has frequently been perceived as less resource intensive than the forward dynamics approach, a new generic algorithm for insufficiently constrained, under-actuated dynamic systems has been developed and thoroughly tested to comply with all numerical restrictions of the enveloping optimization algorithm. Based on this contribution, various optimal control problems for the humanoid platform HRP-2 14 have been formulated to assess the influence of different biologically inspired optimization criteria on the final motion characteristics of walking motions. From thorough bibliographic researches a dynamically more accurate model was comprised, by taking into account the impact absorbing element in the ankle joint complex. Based on the experiences of the previous study, a problem formulation for the limiting case of, dynamically overstepping an obstacle of 20cm x 11cm (height x width) with only two steps, while maintaining its stable operation was accomplished. This is a new record for this platform. In a further part, this work proposes an iterative comprehensive model-based optimal control approach for the conception of a lower limb exo-skeleton that respects the integrated nature of such a mechatronic device. In this contribution, a human effectively wearing such a lower limb exo-skeleton is modeled. The approach then substantiates all system components in an iterative procedure, based on the complete system model, effectively resolving all complex inter-dependencies between the different components of the system. The study in this work is conducted on an important benchmark motion, walking, of a healthy human being. From this study the limiting characteristics of the system are determined and substantial propositions to the realization of various system components are formulated
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