cs.RO · 2026-06-28 · No. 37
Robotics, 2026-06-28.
8 new papers in cs.RO. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
8 entries-
01
E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation
Wen Ye, Peiyan Li, Tingyu Yuan, Yuan Xu, Xiangnan Wu, Chaoyang Zhao, Jing Liu, Nianfeng Liu, Yan Huang, Liang Wang
cs.RO · cs.AI
Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential, as embodied tasks are inherently long-horizon and sequential, making sole reliance on current observations for action scaling inadequate due to...
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02
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Junhao Shi, Zezheng Huai, Siyin Wang, Jia Chen, Yubang Wang, Zhaoye Fei, Hechang Chen, Jingjing Gong, Xipeng Qiu,...
cs.RO · cs.AI
Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from physical failures that inevitably arise over extended operation. Existing systems treat these as separate problems: VLM-based planners lack a unified cyber-physical action space, agent frameworks accumulate...
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03
Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
Ilia Larchenko
cs.RO · cs.AI · cs.LG
I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding. The system placed 1st of 62 teams in the online (simulation) round and 2nd in the real-world final. It improves a vision-language-action (VLA) policy with a reinforcement-learning loop. The policy is its own value function: the same network that predicts actions also predicts success, progress, and a few task-relevant future quantities,...
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04
In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics
Xiaomeng Fu, Junfan Lin, Yang Liu, Yaowei Wang, Guanbin Li, Liang Lin, Ziliang Chen
cs.RO · cs.AI
Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off between semantic fidelity and physical realism. Large language model (LLM)-based approaches can interpret diverse open-vocabulary instructions and compose high-level action plans, but they often generate motions that violate physical constraints. Physics-aware models improve realism through...
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05
Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling
Daosheng Qiu, Haozhuang Chi, Hao Su, Shu Long, Xinyue Miao, Yongle Dong, Wei Zhang
cs.RO · cs.AI
Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states. Large vision-language models provide broad multimodal priors, but their latency and limited reliability in this setting make them unsuitable as always-on in-cabin monitors. We propose a cost-aware selective inference framework for deployable multimodal driver monitoring. The core system is a...
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06
Learning Motion Feasibility from Point Clouds in Cluttered Environments
Sajid Ansari, Arthi, Girish Varma, Antony Thomas
cs.RO · cs.AI
Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this problem in cluttered environments is that infeasible planning attempts by Sampling-based motion planners (SBMPs) can incur substantial computational cost. Also existing approaches for infeasibility certification are limited to low-dimensional configuration spaces and often assume simplified...
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07
LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction
Sangjin Han, Hoseong Jung, Jeongtae Her, Changhyun Choi, H. Jin Kim
cs.RO · cs.AI
Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes. Consequently, predicted trajectories may violate physical and logical constraints, making the prediction set...
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08
IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control
Chenlong Liu, Zhuohui Zhang, Xinyan Chen, Zhipeng Wang, Bin Cheng, Bin He
cs.RO · cs.AI
Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based methods. However, learning-based methods often struggle with sim-to-real transfer because they rely on accurate dynamics modeling or system identification and learn policies in low-level control spaces that are highly sensitive to dynamics mismatch, making them costly and fragile in complex...
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