A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots

• URL: http://arxiv.org/abs/2606.26425v1
• Date: Wed, 24 Jun 2026 22:30:08 GMT
• Title: A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots
• Abstract summary: This thesis presents a robot-local, runtime-editable behavior authoring and runtime system.<n>Our system strives to be maximally observable, predictable, and directable following Coactive Design principles.<n>Our system has been deployed on many humanoid robots, such as Boston Dynamics' DRC Atlas, NASA's Valkyrie, IHMC and Boardwalk Robotics' Nadia, Unitree's H1-2, and IHMC's Alex.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humanoid robots could take on physically demanding, hazardous, and repetitive work in spaces built for humans. However, a useful robot for these spaces must coordinate locomotion, whole body motion, perception, contact, and operator supervision. This thesis presents a robot-local, runtime-editable behavior authoring and runtime system. Our system strives to be maximally observable, predictable, and directable following Coactive Design principles developed during the DARPA Robotics Challenge. Our operator interface remains continuously synchronized to the robot for runtime authoring, monitoring, and repair. Our behavior architecture uniquely combines object-centric Affordance Templates, organization and logic inspired by Behavior Trees, and runtime-editable perception through a behavior scene and primitive scene actions. Action primitives build on a whole-body controller that supports moving the arms while walking, and use a concurrent action layering algorithm for speed. The behavior library developed during this work covers more than twenty real-robot task variants, including push and pull doors with knob, push-bar, and lever-handle mechanisms, multi-step exploration sequences, obstacle clearing, and reactive table-to-table manipulation tasks. This behavior system has been deployed on many humanoid robots, such as Boston Dynamics' DRC Atlas, NASA's Valkyrie, IHMC and Boardwalk Robotics' Nadia, Unitree's H1-2, and IHMC's Alex. We evaluate our system across capability, speed, reliability, and speed of behavior creation, adaptation, extension, and combination. Our experiments demonstrate that we can adapt, extend, and combine existing behaviors to create novel loco-manipulation behaviors in minutes or hours. Videos: https://www.youtube.com/playlist?list=PLJK5CTyotYqsfgfnXb-09YNFeBose6uEY.

Related papers

• A System for Fast, Resilient, and Adaptable Loco-Manipulation Behaviors on Humanoid Robots [0.767210236542832]
  A useful robot must coordinate locomotion, whole-body motion, perception, contact, and operator supervision.<n>We present a robot-local, runtime-editable behavior authoring and runtime system that addresses these challenges.
  arXiv  Detail & Related papers  (2026-09-01T16:47:00Z)
• ZeroWBC: Learning Natural Visuomotor Humanoid Control Directly from Human Egocentric Video [52.78703020909145]
  We introduce ZeroWBC, a novel framework that learns a natural humanoid visuomotor control policy directly from human egocentric videos.<n>Our approach first fine-tunes a Vision-Language Model (VLM) to predict future whole-body human motions based on text instructions and egocentric visual context.<n>Experiments on the Unitree G1 humanoid robot demonstrate that our method outperforms baseline approaches in motion naturalness and versatility.
  arXiv  Detail & Related papers  (2026-03-10T04:19:43Z)
• HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos [74.43500240121476]
  We present HumanX, a full-stack framework that compiles human video into generalizable, real-world interaction skills for humanoids.<n>HumanX achieves over 8 times higher generalization success than prior methods.
  arXiv  Detail & Related papers  (2026-02-02T18:53:01Z)
• FRoM-W1: Towards General Humanoid Whole-Body Control with Language Instructions [147.04372611893032]
  We present FRoM-W1, an open-source framework designed to achieve general humanoid whole-body motion control using natural language.<n>We extensively evaluate FRoM-W1 on Unitree H1 and G1 robots.<n>Results demonstrate superior performance on the HumanML3D-X benchmark for human whole-body motion generation.
  arXiv  Detail & Related papers  (2026-01-19T07:59:32Z)
• TWIST: Teleoperated Whole-Body Imitation System [28.597388162969057]
  We present the Teleoperated Whole-Body Imitation System (TWIST), a system for humanoid teleoperation through whole-body motion imitation.<n>We develop a robust, adaptive, and responsive whole-body controller using a combination of reinforcement learning and behavior cloning.<n>TWIST enables real-world humanoid robots to achieve unprecedented, versatile, and coordinated whole-body motor skills.
  arXiv  Detail & Related papers  (2025-05-05T17:59:03Z)
• HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit [52.12750762494588]
  This paper introduces HOMIE, a semi-autonomous teleoperation system.<n>It combines a reinforcement learning policy for body control mapped to a pedal, an isomorphic exoskeleton arm for arm control, and motion-sensing gloves for hand control.<n>The system is fully open-source, demos and code can be found in our https://homietele.org/.
  arXiv  Detail & Related papers  (2025-02-18T16:33:38Z)
• Know your limits! Optimize the robot's behavior through self-awareness [11.021217430606042]
  Recent human-robot imitation algorithms focus on following a reference human motion with high precision. We introduce a deep-learning model that anticipates the robot's performance when imitating a given reference. Our Self-AWare model (SAW) ranks potential robot behaviors based on various criteria, such as fall likelihood, adherence to the reference motion, and smoothness.
  arXiv  Detail & Related papers  (2024-09-16T14:14:58Z)
• Controlling diverse robots by inferring Jacobian fields with deep networks [48.279199537720714]
  Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics.<n>We introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field.<n>Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
  arXiv  Detail & Related papers  (2024-07-11T17:55:49Z)
• Hierarchical Procedural Framework for Low-latency Robot-Assisted Hand-Object Interaction [45.256762954338704]
  We propose a hierarchical procedural framework to enable robot-assisted hand-object interaction (HOI)<n>An open-loop hierarchy leverages the RGB-based 3D reconstruction of the human hand, based on which motion primitives have been designed to translate hand motions into robotic actions.<n>A case study of ring-wearing tasks indicates the potential application of this work in assistive technologies such as healthcare and manufacturing.
  arXiv  Detail & Related papers  (2024-05-29T21:20:16Z)
• HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
  We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands. Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
  arXiv  Detail & Related papers  (2024-03-15T17:45:44Z)
• Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
  We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
  arXiv  Detail & Related papers  (2022-03-29T15:03:05Z)

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.