cs.RO · 2026-07-14 · No. 53
Robotics, 2026-07-14.
14 new papers in cs.RO. Titles, authors,
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01 — The papers
14 entries-
01
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Yunhai Feng, Natalie Leung, Jiaxuan Wang, Lujie Yang, Haozhi Qi, Preston Culbertson
cs.RO · cs.AI · cs.LG
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation? The answer is not obvious, as manipulation involves complex, contact-rich dynamics and requires delicate regulation of contact modes and forces. We present REGRIND, a minimalist...
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02
NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception
Zhiyang Dou, John U. Onyemelukwe, Hangxing Zhang, Heng Zhang, Minghao Guo, Yunsheng Tian, Michal Piotr Lipiec,...
cs.RO · cs.CV · cs.GR · cs.LG
Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error. This is particularly acute on low-cost platforms, where the linear current-to-torque relation $τ= K_tI$ becomes unreliable during commanded-target tracking because of friction, hysteresis, backlash, and thermal effects. We present NeuralActuator, a neural actuator model that jointly predicts...
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03
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
Yuanzhi Liang, Xufeng Zhan, Haibin Huang, Chi Zhang, Xuelong Li
cs.RO · cs.AI
Artificial general intelligence ultimately requires agents that can reason and act in the physical world. Action models, vision-language-action policies, and world models have advanced this goal, while World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences. However, progress remains fragmented: models use incompatible action spaces and prediction targets, datasets and...
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04
Self-Healing Visual Recovery for Autonomous Ground Vehicles Using Camera-Only Visual Odometry
Jakob Solberg Berntzen, Safia Fatima, Leon Moonen
cs.RO · cs.LG · cs.SE
Low-cost unmanned ground vehicles are often used in indoor places like warehouses, inspection corridors, and farm rows, where painted floor lines guide the robot. Line following is useful because it only needs one camera and little computing power, but it can fail when the line is blocked or turns sharply and goes out of view. Sensor-rich platforms tolerate this through hardware redundancy (LiDAR, GPS, multiple cameras), but camera-only...
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05
Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model
Xinghang Li, Jun Guo, Qiwei Li, Long Qian, Hang Lai, Yueze Wang, Hongyu Yan, Jiahang Cao, Xi Chen, Jingen Qu, Jiaxi...
cs.RO · cs.AI
Recent foundation image and video generation models offer strong generalization and controllability, but their direct application to embodied scenarios is limited by requirements for multi-view consistency, geometric coherence, and robot embodiment constraints. Existing methods typically adapt foundation models with limited robot data, often sacrificing visual knowledge acquired during large-scale pre-training. We present Xiaomi-Robotics-U0,...
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06
SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning
Evelyn D'Elia, Weishu Zhan, Giulio Turrisi, Giulio Romualdi, Giuseppe L'Erario, Raffaello Camoriano, Wei Pan, Daniele Pucci
cs.RO · cs.LG
Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics priors in the learning process. However, most of these approaches are validated on well-defined, low-dimensional benchmark systems rather than high-dimensional robots with complex nonlinear dynamics. In this paper, we introduce \textit{SKooP (Symmetric Koopman...
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07
See like a Robot: Robot-Centric Pointmaps for Vision-Language-Action Models
Byungkun Lee, Dongyoon Hwang, Dongjin Kim, Hojoon Lee, Minho Park, Jaegul Choo
cs.RO · cs.AI
Vision-language-action (VLA) models predict robot actions from visual observations and language instructions. These actions are defined in the robot's own 3D coordinate frame, yet most VLAs observe the scene in the camera frame, creating a frame mismatch between where the scene is observed and where actions are defined. The mismatch is benign under a fixed viewpoint, where the policy can memorize a single observation-to-action mapping, but...
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08
A Glimpse into Long-term Physical Coexistence with Intelligent Robots
Weiqi Jin, Peijun Tang, Kuncheng Luo, Baifu Huang, Binyan Sun, Haotian Yang, Shangjin Xie, Jianan Wang
cs.RO · cs.AI
Long-term physical coexistence with intelligent robots requires more than capable robot policies. A persistent robotic assistant must support diverse user-facing interfaces, maintain long-horizon memory of people and preferences, coordinate across robot embodiments, and translate human intent into safe physical execution. We introduce PHILIA, a multi-robot agent built around a robot gateway abstraction. PHILIA retains the rich interaction and...
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09
Towards Predictive, Aligned, and Scalable Robot Learning
Peijun Tang, Shangjin Xie, Baifu Huang, Binyan Sun, Haotian Yang, Kuncheng Luo, Weiqi Jin, Shilin Fang, Jianan Wang
cs.RO · cs.AI
Learning, at its core, extends beyond memorization to the ability to reason and solve novel problems by navigating a space of possibilities. We introduce Lumo-2, a latent world-action model that generates actions by reasoning over world dynamics in latent space. The learned latent world dynamics capture physically grounded visual transitions, naturally encoding future possibilities and providing a unified substrate for cross-modal alignment....
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10
Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentation
Haojie Huang, Linfeng Zhao, Haotian Liu, Zhang Ye, Si-Yuan Huang, Mingxi Jia, Boce Hu, Fangzhou Lin, Yu Qi, Dian...
cs.RO · cs.AI · cs.LG
Representing manipulation actions as 2D trajectories in the camera plane provides a compact and interpretable basis for learning complex 3D manipulation policies. However, it also creates challenges from out-of-frame trajectories and limited precision. We propose Pix2Act, an imitation learning method that addresses these challenges by generating continuous image-space keypoint trajectories in each camera plane and losslessly recovering...
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11
Comparison-Based Ordinal Learning for Proactive Driving Risk Assessment
Zhuoren Li, Yi Zhong, Weiqi Zhang, Xinrui Zhang, Lu Xiong, Chongfeng Wei, Bo Leng
cs.RO · cs.LG
Real-time driving risk assessment provides an essential basis for proactive safety by identifying and quantifying the danger of ongoing road interactions before adverse outcomes occur. However, due to the scarcity of collision data and frame-level risk labels, existing driving risk assessment methods often rely on surrogate objectives, which may imperfectly align with true collision risk and not faithfully reflect the relative danger of...
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12
VIA: Visual Interface Agent for Robot Control
Hengyuan Hu, Priya Sundaresan, Jensen Gao, Dorsa Sadigh
cs.RO · cs.AI
Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control. General-purpose foundation models (FMs) have grown remarkably capable of some of these, especially vision and reasoning. To leverage this for generalist robot policies, current methods typically involve converting existing FMs into vision-language-action (VLA) models by fine-tuning on robot data to output low-level...
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13
Affordance-Based Manipulation Planning with Text Goals and Sim-to-Real Generalisation via Real-to-Sim Image Conversion
Solvi Arnold, Rin Karashima, Tadashi Adachi, Takafumi Mochizuki, Kimitoshi Yamazaki
cs.RO · cs.AI
We present a manipulation planning system based on affordance recognition and action effect prediction. The system reasons through possible futures in visual form, and evaluates candidate plans by agreement of predicted outcomes with text-based goals set at run-time, using a multi-modal goal-matching module. Positions of objects named in the goal text are tracked through predictions even when occluded, making it possible to generate action...
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14
Think When It Matters: Conditional VLM Reasoning for Social Navigation with RL Policies
Ali Ahmadi, Hamed Rahimi, Adrien Jacquet Cretides, Marie Samson, Mahdi Khoramshahi, Mohamed Chetouani
cs.RO · cs.AI · cs.CV
As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust. While reinforcement learning (RL) navigation policies provide the fast inference and reactive behavior necessary for real-time deployment, they still lack flexible semantic reasoning capabilities and often fail to generalize to complex social scenarios. Recent approaches have...
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