Nonvisual classification of ground condition based on a multimodal sensing approach was investigated for an amoeba-inspired autonomous walking robot. To classify ground condition without image sensing and processing, we implemented artificial proprioception by integrating a three-axis accelerometer, eight foot pressure sensors, and reservoir computing (RC).
arXiv:2608. 05684v1 Announce Type: cross Abstract: Nonvisual classification of ground condition based on a multimodal sensing approach was investigated for an amoeba-inspired autonomous walking robot.
arXiv:2603. 12408v2 Announce Type: replace-cross Abstract: Motion imitation learning (IL) is increasingly used in robotics and human gait modeling, yet its ability to recover biomechanically consistent joint moments without explicit kinetic information remains unclear.
arXiv:2603. 12408v3 Announce Type: replace-cross Abstract: Motion imitation learning (IL) is increasingly used in robotics and human gait modeling, yet its ability to recover biomechanically consistent joint moments without explicit kinetic information remains unclear.
The paper introduces a wireless smart insole that uses 16 pressure sensors and a six‑axis IMU to detect sitting, standing, walking, and unstable walking. Data from 15 healthy adults were processed in overlapping windows and evaluated with participant‑independent cross‑validation, achieving macro‑F1 scores of 0.954–0.980 using Histogram‑Based Gradient Boosting. A compact 1D‑CNN performed similarly but did not significantly outperform the gradient‑boosting model.
arXiv:2606. 15896v1 Announce Type: cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, gait preference, and terrain adaptation within a single optimization objective.
Humanoid motion imitation requires not only accurate perception of human kinematics but also faithful reproduction of physical interactions with the environment. However, existing pipelines rely primarily on vision-based motion capture and kinematic imitation, largely ignoring contact dynamics, leading to artifacts such as foot sliding, floor penetration, and unstable behaviors.
The paper introduces a Reflex-Informed Neuromuscular Reinforcement Learning framework for muscle-driven locomotion. It combines a fixed phase-dependent reflex controller with a reinforcement learning policy that adjusts reflex gains and thresholds for hip swing, knee support, and ankle propulsion. Experiments show the method produces physiologically plausible walking with improved kinematic accuracy, dynamic consistency, symmetry, and stride consistency, and remains robust to muscle weakness and perturbations without retraining.
arXiv:2608. 12145v1 Announce Type: cross Abstract: Autonomous rehabilitation systems must not only recognize human motion but also provide structured feedback to support users without continuous therapist supervision.
FootQuery is a perceptive locomotion framework that retrieves depth information from a robot’s own history by querying each foot’s predicted next touchdown. The policy uses proprioceptive predictions of touchdown locations and uncertainties to sample relevant historical depth frames, fuses these per‑foot features with global visual memory, and generates control actions. In simulation and on a real Unitree G1 robot, FootQuery enables continuous traversal of complex outdoor stairs, indoor routes, platforms, and gaps, outperforming component ablations.
The paper introduces InterTrack, a behavior world model that enables humanoid robots to perform robust whole-body tracking while interacting with varied terrain and objects. Using a Transformer architecture, InterTrack predicts actions, states, and behavior distributions conditioned on the environment, and it is trained with an automated pipeline that reconstructs 3D support geometry from retargeted motions. The system achieves an 81.3% success rate on terrain interaction, a 99.3% fall-recovery rate, and outperforms leading baselines in both free-space tracking and cross-terrain scenarios.
arXiv:2603.26687v2 Announce Type: replace-cross Abstract: Hybrid aerial--ground robots can use thrust to cross obstacles that impede wheel-driven motion, but deciding how much thrust to apply during...
arXiv:2608. 05975v1 Announce Type: cross Abstract: In this paper, we present TRACE (Tokenized Robust Attention for Contact-Aware Estimation), an end-to-end learned proprioceptive odometry estimator for legged robots under unreliable contact conditions.