PILOT: A Perceptive Integrated Low-level Controller for Loco-manipulation over Unstructured Scenes

• URL: http://arxiv.org/abs/2601.17440v1
• Date: Sat, 24 Jan 2026 12:25:20 GMT
• Title: PILOT: A Perceptive Integrated Low-level Controller for Loco-manipulation over Unstructured Scenes
• Abstract summary: PILOT is a single-stage reinforcement learning framework tailored for perceptive loco-manipulation.<n>It synergizes perceptive locomotion and expansive whole-body control within a single policy.<n> PILOT demonstrates superior stability, command tracking precision, and terrain traversability compared to existing baselines.
• Score: 18.916978556216097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humanoid robots hold great potential for diverse interactions and daily service tasks within human-centered environments, necessitating controllers that seamlessly integrate precise locomotion with dexterous manipulation. However, most existing whole-body controllers lack exteroceptive awareness of the surrounding environment, rendering them insufficient for stable task execution in complex, unstructured scenarios.To address this challenge, we propose PILOT, a unified single-stage reinforcement learning (RL) framework tailored for perceptive loco-manipulation, which synergizes perceptive locomotion and expansive whole-body control within a single policy. To enhance terrain awareness and ensure precise foot placement, we design a cross-modal context encoder that fuses prediction-based proprioceptive features with attention-based perceptive representations. Furthermore, we introduce a Mixture-of-Experts (MoE) policy architecture to coordinate diverse motor skills, facilitating better specialization across distinct motion patterns. Extensive experiments in both simulation and on the physical Unitree G1 humanoid robot validate the efficacy of our framework. PILOT demonstrates superior stability, command tracking precision, and terrain traversability compared to existing baselines. These results highlight its potential to serve as a robust, foundational low-level controller for loco-manipulation in unstructured scenes.

Related papers

• VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands [56.029505206531155]
  Vision Guided Agile Interaction Control (VAIC) is a unified framework that bridges the gap between current controllers and humanoid robots.<n>First, a privileged teacher policy masters diverse interaction skills using precise object kinematics and exact environmental states.<n>Second, a deployable student policy distills these capabilities by replacing full body tracking with velocity targets across multiple axes.
  arXiv  Detail & Related papers  (2026-06-08T09:52:55Z)
• Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking [78.30338456206984]
  We introduce a novel framework for training general-purpose 3D foothold-tracking policies.<n>By dynamically providing footstep support through a goal sampler, this method enables the learned policy to be agnostic to specific terrains.<n>Our new target representation effectively mitigates challenges arising in the real world, such as noisy and inaccurate pose estimation and foot contact estimation.
  arXiv  Detail & Related papers  (2026-06-06T16:44:56Z)
• Learning Terrain-Aware Whole-Body Control for Perceptive Legged Loco-Manipulation [2.915167691607446]
  We introduce TA-WBC, a terrain-aware whole-body control framework for legged manipulators.<n>We employ a hybrid exteroception encoder to extract terrain features, providing an essential basis for the robot to proactively adapt posture and footholds.<n>To facilitate stable cross-terrain loco-manipulation, we propose a novel end-effector sampling method based on the foot contact plane.
  arXiv  Detail & Related papers  (2026-05-29T14:22:10Z)
• CEER: Compliant End-Effector and Root Control as a Unified Interface for Hierarchical Humanoid Loco-Manipulation [11.545606851036787]
  We propose a compliant end-effector-root (EE-root) control abstraction for modular humanoid loco-manipulation.<n>EE-root provides a practical abstraction for humanoid loco-manipulation, enabling modular and scalable integration of diverse skills.
  arXiv  Detail & Related papers  (2026-05-19T15:23:40Z)
• ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation [9.708461585583791]
  Collaborative transportation of heavy payloads via loco-manipulation is a challenging yet essential capability for legged robots.<n>This study proposes an Alternating Direction Method of Multipliers-based distributed model predictive control framework for loco-manipulation.
  arXiv  Detail & Related papers  (2026-03-07T08:06:51Z)
• ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation [55.467742403416175]
  We introduce a physics-driven neural algorithm that translates large-scale motion capture to humanoid embodiments.<n>We learn a unified multimodal controller that supports both dense references and sparse task specifications.<n>Results show that ULTRA generalizes to autonomous, goal-conditioned whole-body loco-manipulation from egocentric perception.
  arXiv  Detail & Related papers  (2026-03-03T18:59:29Z)
• Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction [57.252599851031505]
  We introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI.<n>Pro-HOI is a generalizable framework for robust humanoid loco-manipulation.<n> Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness.
  arXiv  Detail & Related papers  (2026-03-01T14:19:59Z)
• Admittance-Based Motion Planning with Vision-Guided Initialization for Robotic Manipulators in Self-Driving Laboratories [0.4899818550820576]
  This paper introduces a motion-planning framework centered on admittance control to enable adaptive and compliant robotic manipulation.<n>Unlike conventional schemes, the proposed approach integrates an admittance controller directly into trajectory execution.<n>This capability enables human operators to override or redirect the robot's motion in real time.
  arXiv  Detail & Related papers  (2026-01-29T13:29:56Z)
• CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation [70.5382178207975]
  hIsight Perturbation (CHIP) is a plug-and-play module that enables controllable end-effector stiffness.<n>CHIP is easy to implement and requires neither data augmentation nor additional reward tuning.<n>We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks.
  arXiv  Detail & Related papers  (2025-12-16T18:56:04Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring [55.159556673975544]
  We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring.<n>Our approach enables robust identification and tracking of large species in their natural habitat.
  arXiv  Detail & Related papers  (2025-08-20T20:05:05Z)
• Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration [8.343657309038285]
  Reinforcement Learning is a powerful framework for developing such robot controllers. We propose a multimodal exploration approach through categorical distributions, which enables us to train planar pushing RL policies. We show that the learned policies are robust to external disturbances and observation noise, and scale to tasks with multiple pushers.
  arXiv  Detail & Related papers  (2023-08-04T16:55:00Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.