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Found 7 papers, 0 papers with code
SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by Animal Learning
Our experimental results indicate that SATA demonstrates remarkable compliance and safety, even in challenging environments such as soft/slippery terrain or narrow passages, and under significant external disturbances, highlighting its potential for practical deployments in human-centric and safety-critical scenarios.
CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation
Enabling legged robots to perform non-prehensile loco-manipulation is crucial for enhancing their versatility.
RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots.
Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network
Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Peclet number.
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon.
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion
These reference motions serve as targets for the RL policy to imitate, leading to the development of robust control policies that can be learned with reliability.
