208 research outputs found
Versatile Reactive Bipedal Locomotion Planning Through Hierarchical Optimization
© 2019 IEEE. When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies
Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbations
This study proposes a novel planning framework based on a model predictive
control formulation that incorporates signal temporal logic (STL)
specifications for task completion guarantees and robustness quantification.
This marks the first-ever study to apply STL-guided trajectory optimization for
bipedal locomotion push recovery, where the robot experiences unexpected
disturbances. Existing recovery strategies often struggle with complex task
logic reasoning and locomotion robustness evaluation, making them susceptible
to failures caused by inappropriate recovery strategies or insufficient
robustness. To address this issue, the STL-guided framework generates optimal
and safe recovery trajectories that simultaneously satisfy the task
specification and maximize the locomotion robustness. Our framework outperforms
a state-of-the-art locomotion controller in a high-fidelity dynamic simulation,
especially in scenarios involving crossed-leg maneuvers. Furthermore, it
demonstrates versatility in tasks such as locomotion on stepping stones, where
the robot must select from a set of disjointed footholds to maneuver
successfully
Motion Planning and Control of Dynamic Humanoid Locomotion
Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot.
Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d.
As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics.
The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end
Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante
State-of-the-art reinforcement learning is now able to learn versatile
locomotion, balancing and push-recovery capabilities for bipedal robots in
simulation. Yet, the reality gap has mostly been overlooked and the simulated
results hardly transfer to real hardware. Either it is unsuccessful in practice
because the physics is over-simplified and hardware limitations are ignored, or
regularity is not guaranteed, and unexpected hazardous motions can occur. This
paper presents a reinforcement learning framework capable of learning robust
standing push recovery for bipedal robots that smoothly transfer to reality,
providing only instantaneous proprioceptive observations. By combining original
termination conditions and policy smoothness conditioning, we achieve stable
learning, sim-to-real transfer and safety using a policy without memory nor
explicit history. Reward engineering is then used to give insights into how to
keep balance. We demonstrate its performance in reality on the lower-limb
medical exoskeleton Atalante
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