Vijayakumar, Sethu

Mistry, Michael

Merkt, Wolfgang Xaver

Engineering and Physical Sciences Research Council (EPSRC)

2020-05-18T11:07:09Z

2020-05-18T11:07:09Z

2020-06-25

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.

https://hdl.handle.net/1842/37057

http://dx.doi.org/10.7488/era/358

en

The University of Edinburgh

Ferrolho, H., Merkt, Wolfgang, Yang, Y., Ivan, V., and Vijayakumar, S. (2018). WholeBody End-Pose Planning for Legged Robots on Inclined Support Surfaces in Complex Environments. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages 944–95

Ivan, V., Yang, Y., Merkt, Wolfgang, Camilleri, M. P., and Vijayakumar, S. (2019). EXOTica: An Extensible Optimization Toolset for Prototyping and Benchmarking Motion Planning and Control. In Koubaa, A., editor, Robot Operating System (ROS): The Complete Reference (Volume 3), pages 211–240. Springer International Publishing, Cham

Mastalli, C., Budhiraja, R., Merkt, Wolfgang, Saurel, G., Hammoud, B., Naveau, M., Carpentier, J., Righetti, L., Vijayakumar, S., and Mansard, N. (2020). Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)

Mower, C. E., Merkt, Wolfgang, and Vijayakumar, S. (2019). Comparing Alternate Modes of Teleoperation for Constrained Tasks. In Proceedings of the IEEE Conference on Automation Science and Engineering (CASE), pages 1497–1504

Merkt, Wolfgang, Ivan, V., and Vijayakumar, S. (2018). Leveraging Precomputation with Problem Encoding for Warm-Starting Trajectory Optimization in Complex Environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5877–5884.

Merkt, Wolfgang, Ivan, V., and Vijayakumar, S. (2019a). Continuous-Time Collision Avoidance for Trajectory Optimization in Dynamic Environments. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7248–7255.

Merkt, Wolfgang, Ivan, V., Yang, Y., and Vijayakumar, S. (2019). Towards Shared Autonomy Applications using Whole-body Control Formulations of Locomanipulation. In Proceedings of the IEEE Conference on Automation Science and Engineering (CASE), pages 1206–1211.

Merkt, Wolfgang, Yang, Y., Stouraitis, T., Mower, C. E., Fallon, M., and Vijayakumar, S. (2017). Robust Shared Autonomy for Mobile Manipulation with Continuous Scene Monitoring. In Proceedings of the IEEE Conference on Automation Science and Engineering (CASE), pages 130–137

Yang, C., Yuan, K., Merkt, Wolfgang, Komura, T., Vijayakumar, S., and Li, Z. (2018). Learning Whole-Body Motor Skills for Humanoids. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages 270–276

Yang, Y., Ivan, V., Merkt, Wolfgang, and Vijayakumar, S. (2016). Scaling sampling based motion planning to humanoid robots. In Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1448–1454

Yang, Y., Merkt, Wolfgang, Ferrolho, H., Ivan, V., and Vijayakumar, S. (2017). Efficient Humanoid Motion Planning on Uneven Terrain Using Paired Forward-Inverse Dynamic Reachability Maps. IEEE Robotics and Automation Letters, 2(4):2279–2286

Yang, Y., Merkt, Wolfgang, Ivan, V., Li, Z., and Vijayakumar, S. (2018). HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes. IEEE Robotics and Automation Letters, 3(1):551–558.

Yang, Y., Merkt, Wolfgang, Ivan, V., and Vijayakumar, S. (2018). Planning in TimeConfiguration Space for Efficient Pick-and-Place in Non-Static Environments with Temporal Constraints. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages 1–9.

motion planning

optimisation

trajectory optimisation

complex environment

shared environment

humanoid

quadruped

robot

initialisation

memory of motion

Experience-driven optimal motion synthesis in complex and shared environments

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy