Reconstructing null-space policies subject to dynamic task constraints in redundant manipulators

We consider the problem of direct policy learning in situations where the policies are only observable through their projections into the null-space of a set of dynamic, non-linear task constraints. We tackle the issue of deriving consistent data for the learning of such policies and make two contributions towards its solution. Firstly, we derive the conditions required to exactly reconstruct null-space policies and suggest a learning strategy based on this derivation. Secondly, we consider the case that the null-space policy is conservative and show that such a policy can be learnt more easily and robustly by learning the underlying potential function and using this as our representation of the policy.

Reconstructing Null-space Policies Subject to Dynamic Task Constraints in Redundant Manipulators

We consider the problem of direct policy learning in situations where the policies are only observable through their projections into the null-space of a set of dynamic, non-linear task constraints. We tackle the issue of deriving consistent data for the learning of such policies and make two contributions towards its solution. Firstly, we derive the conditions required to exactly reconstruct null-space policies and suggest a learning strategy based on this derivation. Secondly, we consider the case that the null-space policy is conservative and show that such a policy can be learnt more easily and robustly by learning the underlying potential function and using this as our representation of the policy

A generic optimization-based framework for reactive collision avoidance in bipedal locomotion

In this work we present a novel and generic framework for reactive collision avoidance in bipedal locomotion, which is formulated as an optimization problem considering the constraints of collision avoidance as well as others (e.g. joint limits) to simultaneously satisfy both Cartesian and joint space objectives. To realize the reactive behaviors, several task space motions, such as the translational motion of the swing foot and the vertical position of the support foot, could be relaxed in presence of obstacles. Therefore, the swing foot trajectory is modulated with respect to the references in real-time for preventing future collisions between the legs, or legs and obstacles in the environment. External obstacle negotiation in the proposed framework can also be addressed generically by treating the obstacle as an extended segment of the support foot. The allowable deviation of the relaxed degrees of freedom from their references could be further utilized to modify the foot placement to regenerate a reactive walking pattern. The validation and the performance of the proposed method are fully evaluated and demonstrated in physics based simulations of the compliant humanoid robot COMAN

Imitating human motion using humanoid upper body models

Includes abstract.Includes bibliographical references.This thesis investigates human motion imitation of five different humanoid upper bodies (comprised of the torso and upper limbs) using human dance motion as a case study. The humanoid models are based on five existing humanoids, namely, ARMAR, HRP-2, SURALP, WABIAN-2, and WE-4RII. These humanoids are chosen for their different structures and range of joint motion

Improvement of a Multi-Body Collision Computation Framework and Its Application to Robot (Self-)Collision Avoidance

One of the fundamental demands on robotic systems is a safe interaction with their environment. In order to fulfill that condition, both collisions with obstacles and own structure have to be avoided. This problem has been addressed before at the German Aerospace Center (DLR) through the use of different algorithms. In this work, a novel solution that differentiates itself from previous implementations due to its geometry-independent, flexible thread structure and computationally robust nature is presented. In a first step, in order to achieve self-collision avoidance, collision detection must be handled. In this line, the Robotics and Mechatronics Center of the DLR developed its own version of the Voxmap-Pointshell (VPS) Algorithm. This penalty based collision computation algorithm uses two types of haptic data structures for each pair of potentially colliding objects in order to detect contact points and compute forces of interfering virtual objects; voxelmaps and pointshells. Prior to the work presented, a framework for multi-body collision detection already existed. However, it was not designed nor optimized to handle mechanisms. This thesis resents a framework that handles collision detection, force computation and physics processing of multi-body virtual realities in real-time integrating the DLR VPS Algorithm implementation. Due to the high number of available robots and mechanisms, a method that is both robust and generic enough to withstand the forthcoming developments would be desirable. In this work, an input configuration file detailing the mechanism’s structure is used, based on the Denavit-Hartenberg convention, so that any type of robotic system or virtual object can use this method without any loss of validity. Experiments to prove the validity of this work have been performed both on DLR’s HUG simulator and on DLR’s HUG haptic device, composed of two DLR-KUKA light weight robots (LWRs)

Experience-driven optimal motion synthesis in complex and shared environments

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

A Stability-Estimator to Unify Humanoid Locomotion: Walking, Stair-Climbing and Ladder-Climbing

The field of Humanoid robotics research has often struggled to find a unique niche that is not better served by other forms of robot. Unlike more traditional industrials robots with a specific purpose, a humanoid robot is not necessarily optimized for any particular task, due to the complexity and balance issues of being bipedal. However, the versatility of a humanoid robot may be ideal for applications such as search and rescue. Disaster sites with chemical, biological, or radiation contamination mean that human rescue workers may face untenable risk. Using a humanoid robot in these dangerous circumstances could make emergency response faster and save human lives. Despite the many successes of existing mobile robots in search and rescue, stair and ladder climbing remains a challenging task due to their form. To execute ladder climbing motions effectively, a humanoid robot requires a reliable estimate of stability. Traditional methods such as Zero Moment Point are not applicable to vertical climbing, and do not account for force limits imposed on end-effectors. This dissertation implements a simple contact wrench space method using a linear combination of contact wrenches. Experiments in simulation showed ZMP equivalence on flat ground. Furthermore, the estimator was able to predict stability with four point contact on a vertical ladder. Finally, an extension of the presented method is proposed based on these findings to address the limitations of the linear combination.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201