Q-Planning integrates a frozen behavior cloning policy with a small off-policy Q-function, enabling stable self-improvement and success rates up to 99%.

Varun Giridhar, Anant Khandelwal, Jeremy A. Collins et al.

SRL-MPC integrates high-order control barrier functions with reinforcement learning for shape-aware multi-robot navigation, achieving 100% success in dense, complex scenarios.

Ruihua Han, Rui Gao, Zhe Liu et al.

Proposes Video2DoorTraversal, reconstructing instance-aligned, simulation-ready doors from a single RGB video, achieving 96.57% success in real-world door traversal.

Xincheng Tang, Yiji Chen, Youhan Xie et al.

PVRA: A pointwise voting framework using 3D keypoints, achieving 89.29% Step [email protected] on assembly pose estimation.

Kulunu Samarawickrama, Roel Pieters

PartialBiGrasp leverages convolutional occupancy networks to infer hidden local geometry from partial point clouds, enabling stable dual-arm grasping with 55.16% force-closure rate and 67.87% success on DG16M.

Ayush Kaura, Vignesh Vembar, Md Faizal Karim et al.

DA-WAM introduces decision-aligned future latent representations for autonomous driving, achieving state-of-the-art performance with 93.7 PDMS on NAVSIM-v1, by coupling action-conditioned future prediction with trajectory scoring.

Ruiguo Zhong, Benshan Ma, Xiaolong Chen et al.

LabDex evaluates dexterous lab manipulation through hierarchical tasks, validating its effectiveness in real and simulated environments.

Zhipeng Tang, Sihang Chen, Sha Zhang et al.

GAPL integrates LLM estimation, simulation grounding, and PPO optimization to improve trajectory planning, reducing collision rate by 0.76 and increasing reward by 1.44.

Zhihong Cui, Hengyu Liu, Zhangkai Wu et al.

This paper introduces ControlledShifts, a framework for standardized robustness evaluation in trajectory prediction under distribution shifts, combining a novel bias-controlled data splitting method with a unified performance score.

Ingrid navarro, Pablo Ortega-Kral, Yutong Duan et al.

Proposes ESTI to detect state-semantic injection in LLM-driven embodied agents, evaluating downstream propagation of false environment states.

Jiawei Liu, Jiacheng Guo, Tian Zhang et al.

Neurosymbolic embodied agent combines visual exploration and symbolic planning, achieving over 90% success in household tasks.

Mohammad Albinhassan, Yuming Feng, Alessandra Russo et al.

This study evaluates how identity-conditioned prompts affect LLM-generated surveillance robot descriptions using six readability metrics, revealing systematic variations.

Nneka Hyman, Jasmine Khan, Raj Korpan

RAPAC-DP introduces response-aligned pending-action compensation, retaining 81.4% performance at maximum delay, improving robustness of diffusion policies under communication latency.

Tao Wang, Wei Wang, Jianhui Wang et al.

Proposes low-rank (Rank4) latent carriers enabling autonomous counterfactual rollouts in 192D recurrent world models, validated on a two-object collision environment.

Yang Liu, Yuming Chen

Active inference-based EFE path planning enables autonomous robots to efficiently map and locate high-value regions under resource constraints, outperforming traditional methods.

Ajith Anil Meera, Pablo Lanillos, Wouter Kouw

Multi-layer twisted yarn capacitive sensors achieve a sensitivity of 0.1331 MPa$^{-1}$ at 100kHz, with proximity detection ranges of 40-60mm, enabling tunable pressure and near-field sensing.

Ishtia Zahir, Eslam Saleh, Maryam Rezayati et al.

HumanScore, a preference-trained metric, uses 153 hours of diverse motion data to improve human-aligned humanoid motion tracking evaluation.

Dairu Liu, Zekun Qi, Jiayu Zeng et al.

The EDD framework employs explicit environmental-social disentanglement for continual learning of socially appropriate robot actions, outperforming state-of-the-art baselines.

Rafal Robert Karpinski, Fethiye Irmak Dogan, Nikhil Churamani et al.

ContactGuard employs action-conditioned latent world models to predict imminent contact failures, enabling pre-contact aborts and enhancing robotic manipulation safety.

Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi

Seeker learns control-relevant visual regions from action supervision, boosting robot task success from 48.3% to 76.7%.

Zheyu Zhuang, Ruiyu Wang, Nick Heppert et al.