REVIEW 15 cited by
Visual Whole-Body Control for Legged Loco-Manipulation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We study the problem of mobile manipulation using legged robots equipped with an arm, namely legged loco-manipulation. The robot legs, while usually utilized for mobility, offer an opportunity to amplify the manipulation capabilities by conducting whole-body control. That is, the robot can control the legs and the arm at the same time to extend its workspace. We propose a framework that can conduct the whole-body control autonomously with visual observations. Our approach, namely Visual Whole-Body Control(VBC), is composed of a low-level policy using all degrees of freedom to track the body velocities along with the end-effector position, and a high-level policy proposing the velocities and end-effector position based on visual inputs. We train both levels of policies in simulation and perform Sim2Real transfer for real robot deployment. We perform extensive experiments and show significant improvements over baselines in picking up diverse objects in different configurations (heights, locations, orientations) and environments.
Forward citations
Cited by 15 Pith papers
-
Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design
A single diffusion transformer trains on tokenized robot bodies and motions to generate and optimize robot designs for unseen rewards and trajectories, outpacing evolutionary search in speed and often in reward.
-
FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation
FT-WBC introduces a decoupled policy architecture with a Fault Estimator and Posture Adaptation Module that converts unstable arm-driven posture requests into safe base commands under actuator failures in legged manipulators.
-
OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
OpenHLM is an empirical recipe yielding a whole-body humanoid VLA model that outperforms GR00T N1.6 and Ψ0 baselines on long-horizon tasks using less than half the demonstration time.
-
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
StereoPolicy fuses stereo image pairs via a Stereo Transformer on pretrained 2D encoders to boost robotic manipulation policies, showing gains over monocular, RGB-D, point cloud, and multi-view methods in simulations ...
-
SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision
SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active s...
-
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A hierarchical tactile-aware policy trained from human demos and sim RL improves real quadrupedal loco-manipulation by 28.54% on average over vision-only and visuotactile baselines.
-
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A tactile-aware hierarchical policy for quadrupedal loco-manipulation improves real-world contact-rich task performance by 28.54% over vision-only and visuotactile baselines.
-
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.
-
UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies
Embodiment-Aware Diffusion Policy steers a UMI-trained diffusion policy with controller tracking-cost gradients at inference time, improving aerial manipulation success in simulation and real flights.
-
TOP: Time Optimization Policy for Stable and Accurate Standing Manipulation with Humanoid Robots
A reinforcement-learned time optimization policy that adaptively slows upper-body motion clips improves stability and precision of humanoid standing manipulation at a modest time cost.
-
FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation
FT-WBC is a decoupled-policy framework that uses fault estimation and posture adaptation to synthesize compensatory gaits and preserve arm workspace in legged manipulators under actuator failures.
-
Learning Terrain-Aware Whole-Body Control for Perceptive Legged Loco-Manipulation
TA-WBC is a terrain-aware RL policy for legged loco-manipulation using exteroception, contact-plane sampling, and distillation to improve reachable space, tracking, and stability across terrains.
-
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
A hierarchical tactile-aware policy combines human-demonstration training for contact cue prediction with sim-to-real reinforcement learning to improve quadrupedal loco-manipulation performance by 28.54% over vision b...
-
Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning
A single goal-conditioned RL policy trained on contact plans performs multiple gaits and bimanual manipulation tasks on quadruped and humanoid robots.
-
StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception
StereoPolicy fuses left-right image features via cross-attention to deliver consistent gains over RGB, RGB-D, point cloud, and multi-view baselines in simulation and real-robot manipulation tasks.
Discussion (0). Sign in to comment.