Library
Rl for control
25 published papers.
5:32
Multi-critic Learning for Whole-body End-effector Twist Tracking
RL for control · deck
5:21
Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
RL for control · deck
10:43
TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies
RL for control · deck
6:35
ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations
RL for control · deck
6:33
Divide, Discover, Deploy: Factorized Skill Learning with Symmetry and Style Priors
RL for control
6:20
Non-conflicting Energy Minimization in Reinforcement Learning based Robot Control
RL for control
7:21
LocoFormer: Generalist Locomotion via Long-context Adaptation
RL for control · deck
5:12
HuB: Learning Extreme Humanoid Balance
RL for control · deck
5:54
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
RL for control · deck
6:56
Steering Your Diffusion Policy with Latent Space Reinforcement Learning
RL for control · deck
4:51
WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
RL for control · deck
8:56
Kernel-Based Safe Exploration in Deep Reinforcement Learning
RL for control · deck
6:10
How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints
RL for control · deck
6:58
TD-M(PC)²: Improving Temporal Difference MPC Through Policy Constraint
Model predictive control · deck
9:20
Model-Based Reinforcement Learning under Random Observation Delays
RL for control · deck
7:35
Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making
Model predictive control · deck
5:41
Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning
Mobile manipulation · deck
6:16
HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincaré Maps, and Regions of Attraction
RL for control · deck
5:38
Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics
RL for control · deck
6:38
OffRIPP: Offline RL-based Informative Path Planning
RL for control · deck
5:39
ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
Distributed control · deck
5:48
Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids
Collaboration · deck
7:55
CASH: Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination
Collaboration · deck
6:28
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning
RL for control · deck
4:40
Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System
RL for control
2201.04180