793 research outputs found
Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
Humanoid robots dynamically navigate an environment by interacting with it
via contact wrenches exerted at intermittent contact poses. Therefore, it is
important to consider dynamics when planning a contact sequence. Traditional
contact planning approaches assume a quasi-static balance criterion to reduce
the computational challenges of selecting a contact sequence over a rough
terrain. This however limits the applicability of the approach when dynamic
motions are required, such as when walking down a steep slope or crossing a
wide gap. Recent methods overcome this limitation with the help of efficient
mixed integer convex programming solvers capable of synthesizing dynamic
contact sequences. Nevertheless, its exponential-time complexity limits its
applicability to short time horizon contact sequences within small
environments. In this paper, we go beyond current approaches by learning a
prediction of the dynamic evolution of the robot centroidal momenta, which can
then be used for quickly generating dynamically robust contact sequences for
robots with arms and legs using a search-based contact planner. We demonstrate
the efficiency and quality of the results of the proposed approach in a set of
dynamically challenging scenarios
Learning for Humanoid Multi-Contact Navigation Planning
Humanoids' abilities to navigate uneven terrain make them well-suited for disaster response efforts, but humanoid motion planning in unstructured environments remains a challenging problem. In this dissertation we focus on planning contact sequences for a humanoid robot navigating in large unstructured environments using multi-contact motion, including both foot and palm contacts. In particular, we address the two following questions: (1) How do we efficiently generate a feasible contact sequence? and (2) How do we efficiently generate contact sequences which lead to dynamically-robust motions?
For the first question, we propose a library-based method that retrieves motion plans from a library constructed offline, and adapts them with local trajectory optimization to generate the full motion plan from the start to the goal. This approach outperforms a conventional graph search contact planner when it is difficult to decide which contact is preferable with a simplified robot model and local environment information. We also propose a learning approach to estimate the difficulty to traverse a certain region based on the environment features. By integrating the two approaches, we propose a planning framework that uses graph search planner to find contact sequences around easy regions. When it is necessary to go through a difficult region, the framework switches to use the library-based method around the region to find a feasible contact sequence faster.
For the second question, we consider dynamic motions in contact planning. Most humanoid motion generators do not optimize the dynamic robustness of a contact sequence. By querying a learned model to predict the dynamic feasibility and robustness of each contact transition from a centroidal dynamics optimizer, the proposed planner efficiently finds contact sequences which lead to dynamically-robust motions. We also propose a learning-based footstep planner which takes external disturbances into account. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Neural networks are trained to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate contact sequences robust to external disturbances efficiently.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162908/1/linyuchi_1.pd
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control
We present a unified model-based and data-driven approach for quadrupedal
planning and control to achieve dynamic locomotion over uneven terrain. We
utilize on-board proprioceptive and exteroceptive feedback to map sensory
information and desired base velocity commands into footstep plans using a
reinforcement learning (RL) policy trained in simulation over a wide range of
procedurally generated terrains. When ran online, the system tracks the
generated footstep plans using a model-based controller. We evaluate the
robustness of our method over a wide variety of complex terrains. It exhibits
behaviors which prioritize stability over aggressive locomotion. Additionally,
we introduce two ancillary RL policies for corrective whole-body motion
tracking and recovery control. These policies account for changes in physical
parameters and external perturbations. We train and evaluate our framework on a
complex quadrupedal system, ANYmal version B, and demonstrate transferability
to a larger and heavier robot, ANYmal C, without requiring retraining.Comment: 19 pages, 15 figures, 6 tables, 1 algorithm, submitted to T-RO; under
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