Related papers: SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids

SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids

URL: http://arxiv.org/abs/2605.01427v1
Date: Sat, 02 May 2026 12:57:27 GMT
Title: SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids
Abstract summary: SixthSense infers whole-body contact timing, location, and wrenches from proprioception and IMU data alone.<n>It serves as a plug-and-play perception module for applications including collision detection, physical human-robot interaction, and force-feedback teleoperation.
Score: 13.456157050156643
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Humanoid robots are entering our physical world at scale, yet as oversized toys--good at singing and dancing, but short on force-interaction capabilities for practical tasks. Bridging this gap necessitates prioritizing reliable contact perception as a fundamental requirement. Estimating external wrenches in humanoids is complicated by floating-base dynamics and indeterminate contact locations. Existing analytical frameworks require idealistic assumptions and hard-to-obtain measurements, which are often unavailable in practice. To bridge this gap, we propose SixthSense, a task-agnostic approach that infers whole-body contact timing, location, and wrenches from proprioception and IMU data alone. To capture the multi-modal dynamics between unstructured contact inputs and the uncertain motion outputs, we employ conditional flow matching to tokenize proprioceptive histories and estimate a spatiotemporally sparse contact-event flow. SixthSense serves as a plug-and-play perception module for applications including collision detection, physical human-robot interaction, and force-feedback teleoperation. Experiments across standing, walking, and whole-body motion-tracking policies showcased unprecedented performance in diverse behaviors.

Related papers

ThorArena: Benchmarking Humanoid Physical Interaction with Human Motion-Force Demonstrations [24.273078797508884]
ThorArena is a benchmark for evaluating force-aware humanoid interaction based on human demonstrations with synchronized motion and force measurements.<n>We propose force-aware evaluation metrics that jointly assess whole-body tracking accuracy, robustness under different force levels, control effort, and episode survival.
arXiv Detail & Related papers (2026-07-07T09:26:51Z)
Unleashing Guidance Without Classifiers for Human-Object Interaction Animation [54.24065561632133]
Prior diffusion-based approaches often rely on hand-crafted contact priors or human-imposed kinematic constraints to improve contact quality.<n>We propose a data-driven alternative in which guidance emerges from the denoising pace itself, reducing dependence on manually designed priors.<n>We find that this data-driven guidance is inherently contact-aware, and can be enhanced when training is augmented with a broad spectrum of synthetic object geometries.
arXiv Detail & Related papers (2026-03-26T17:59:34Z)
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)
Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations [63.80827184637476]
We introduce D-STAR, a hierarchical policy that disentangles when to act from where to act.<n>We validate our framework through extensive and rigorous simulations.
arXiv Detail & Related papers (2026-01-14T14:37:06Z)
EscherVerse: An Open World Benchmark and Dataset for Teleo-Spatial Intelligence with Physical-Dynamic and Intent-Driven Understanding [56.89359230139883]
We introduce Teleo-Spatial Intelligence (TSI), a new paradigm that unifies two critical pillars: Physical-Dynamic Reasoning and Intent-Driven Reasoning.<n>We present EscherVerse, consisting of a large-scale, open-world benchmark (Escher-Bench), a dataset (Escher-35k), and models (Escher series)<n>It is the first benchmark to systematically assess Intent-Driven Reasoning, challenging models to connect physical events to their underlying human purposes.
arXiv Detail & Related papers (2026-01-04T14:42:39Z)
HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario [63.77482302352545]
HHI-Assist is a dataset comprising motion capture clips of human-human interactions in assistive tasks.<n>Our work has the potential to significantly enhance robotic assistance policies.
arXiv Detail & Related papers (2025-09-12T09:38:17Z)
AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions [13.802675708793014]
Perceiving and interacting with 3D articulated objects, such as cabinets, doors, and faucets, pose particular challenges for future home-assistant robots. We propose a novel framework, named AdaAfford, that learns to perform very few test-time interactions for quickly adapting the affordance priors to more accurate instance-specific posteriors.
arXiv Detail & Related papers (2021-12-01T03:00:05Z)
TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
arXiv Detail & Related papers (2021-04-08T20:01:00Z)

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