WHOLE-MoMa improves whole-body mobile manipulation by applying offline RL with Q-chunking to demonstrations from randomized sub-optimal controllers, outperforming baselines and transferring to real robots without teleoperation or real-world training data.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
fields
cs.RO 6representative citing papers
Reinforcement learning sensorimotor policies enable quadrotors to traverse narrow gaps at extreme tilts with 5 cm clearance using only vision and proprioception, including reactive traversal of moving gaps.
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.
AhaRobot delivers 0.7 mm repeatability on a $1000 bimanual platform using dual-motor compensation and a novel 26-faced marker handle that cuts tracking error 80% versus a 6-faced baseline.
A diffusion policy learns coordinated control of a mobile base and dual arms to open and traverse damped pull doors in a single end-to-end visuomotor model.
RLFP and the FAC algorithm combine foundation-model priors for policy, value, and rewards to produce sample-efficient robotic RL that reaches 86% real-robot success after one hour and 100% success on 7/8 Meta-world tasks in under 100k frames.
citing papers explorer
-
Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers
WHOLE-MoMa improves whole-body mobile manipulation by applying offline RL with Q-chunking to demonstrations from randomized sub-optimal controllers, outperforming baselines and transferring to real robots without teleoperation or real-world training data.
-
Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
Reinforcement learning sensorimotor policies enable quadrotors to traverse narrow gaps at extreme tilts with 5 cm clearance using only vision and proprioception, including reactive traversal of moving gaps.
-
HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.
-
AhaRobot: A Low-Cost Open-Source Bimanual Mobile Manipulator for Embodied AI
AhaRobot delivers 0.7 mm repeatability on a $1000 bimanual platform using dual-motor compensation and a novel 26-faced marker handle that cuts tracking error 80% versus a 6-faced baseline.
-
Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing
A diffusion policy learns coordinated control of a mobile base and dual arms to open and traverse damped pull doors in a single end-to-end visuomotor model.
-
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own
RLFP and the FAC algorithm combine foundation-model priors for policy, value, and rewards to produce sample-efficient robotic RL that reaches 86% real-robot success after one hour and 100% success on 7/8 Meta-world tasks in under 100k frames.