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Found 13 papers, 2 papers with code
Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control
Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP).
Robotics Optimization and Control
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments
We present a novel framework for motion planning in dynamic environments that accounts for the predicted trajectories of moving objects in the scene.
Motion Planning
Robotics
Systems and Control
Systems and Control
A Feasibility-Driven Approach to Control-Limited DDP
Differential dynamic programming (DDP) is a direct single shooting method for trajectory optimization.
Memory Clustering using Persistent Homology for Multimodality- and Discontinuity-Sensitive Learning of Optimal Control Warm-starts
We demonstrate our method on a cart-pole toy problem and a quadrotor avoiding obstacles, and show that clustering samples based on inherent structure improves the warm-start quality.
A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots
In literature, there are multiple model- and learning-based approaches that require significant computational resources to be effectively deployed and they may have limited generality.
Robotics
CPG-ACTOR: Reinforcement Learning for Central Pattern Generators
Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (6x more) compared with previous approaches.
Robotics
Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion
This encourages disentanglement such that application of a drive signal to a single dimension of the latent state induces holistic plans synthesising a continuous variety of trot styles.
Agile Maneuvers in Legged Robots: a Predictive Control Approach
To the best of our knowledge, our predictive controller is the first to handle actuation limits, generate agile locomotion maneuvers, and execute optimal feedback policies for low level torque control without the use of a separate whole-body controller.
VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation
We evaluate our approach on two versions of the real ANYmal quadruped robots and demonstrate that our method achieves a continuous blend of dynamic trot styles whilst being robust and reactive to external perturbations.
Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization
This allows us to obtain locomotion policies that are robust to variations in system dynamics.
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
In this paper, we propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs).
Roll-Drop: accounting for observation noise with a single parameter
This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state.
