Belief-Overlap Partial Inversion: Object-Centric Flow Replanning for Humanoid Loco-Manipulation
cs.RO
Submitted: 2026-10-09
Updated: 2026-10-09
License: http://creativecommons.org/licenses/by/4.0/
The gist: People often learn how an unfamiliar object moves through a small exploratory push, using its response to guide their next motion.
Abstract
People often learn how an unfamiliar object moves through a small exploratory push, using its response to guide their next motion. Inspired by this behavior, we study humanoids pushing carts whose mass and caster stiffness are initially unknown. Our object-centric framework closes online estimation, flow-based planning, and whole-body control into a loop, creating a tension: replacing the plan whenever the belief shifts disrupts the maneuver under way, while keeping it strands the robot on a path its evidence has ruled out. Belief-Overlap Partial Inversion (BOPI) scales revision to agreement, inverting the flow under the old belief only as far as the beliefs disagree before reconstructing under the new one. Across 18 simulated goals it lowers pooled final position and yaw error by roughly 33% and 47% against fixed-depth replanning, and on hardware it translates and turns a non-holonomic cart.
Sources
- VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands
- AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation
- REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
- ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning
- Integrating Maneuverable Planning and Adaptive Control for Robot Cart-Pushing under Disturbances
- Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies
- UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
- Proximal Policy Optimization Algorithms
- mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
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