News & Research

arXiv:2610.28814v1 Announce Type: new Abstract: In contact-rich manipulation, contact conditions can change before a visual policy updates its action plan. Recomputing the plan through the visual policy adds latency, while tactile feedback alone does not specify how the ongoing motion should be revised for the task. We introduce T-WLA, a world-language-action architecture that uses shared visual memory to guide both action generation and tactile correction. At each visual update, the visual branch integrates the current image, task instruction, and retrieved history into a visual memory that conditions base-action generation. The same memory is cached for a lightweight tactile editor, which combines it with the active action chunk and incoming touch to predict residual corrections to unexecuted actions without rerunning the visual branch. Future-image and future-pressure prediction provide training-only supervision, encouraging the visual memory and tactile features to capture interact

arXiv:2610.28769v1 Announce Type: new Abstract: This paper presents a novel framework integrating a robust wrench observer with a hybrid pose and force controller for physical Human-Robot Interaction (pHRI). In pHRI tasks, robots are expected to perform task-space motions robustly while remaining compliance with human disturbance, which proposes distinct control objectives. Most existing approaches focus mainly on either robustness or compliance and rely on force sensors for real-time tracking. To address these limitations, we propose a novel framework with the following key contributions. Firstly, a neural network (NN) is trained to improve the observer performance by compensating for system friction and unmodeled dynamics. Secondly, a task-null-space decomposition is applied to separate the joint velocity and reduce the effect of the human disturbance on the observer. Finally, a hybrid pose and force controller is introduced to achieve the end-effector pose and force tracking. Experi

arXiv:2610.28761v1 Announce Type: new Abstract: In human-robot interaction (HRI) tasks, humans may interact with the robot in either an active guiding or passive following state, resulting in distinct control objectives for each interaction mode. Most existing approaches rely on predefined switching thresholds to distinguish interaction modes, but their performance can be sensitive to measurement noise. To address this limitation, we propose a novel framework with the following key contributions. First, a neural network (NN) trained via meta-learning is developed to recognize human intention without manually defined thresholds. By successfully incorporating force-dependent parameters from the controller, the recognition rates are improved from 90.1\% to 99.1\% for the passive mode and from 79.8\% to 97.1\% for the active mode. Second, an adaptive meta-learning controller is introduced to compensate for human disturbances and enable online adaptation to varying interaction forces. Third

arXiv:2610.28712v1 Announce Type: new Abstract: First-person humanoid teleoperation ties access to side and rear targets to operator rotation. We present coupled visual--motor remapping, pairing a panoramic azimuth warp with its inverse upper-limb yaw transform. A working prototype implements Global Zoom (GZ) and Focus+Context (F+C). In a within-subject study (N=8); 72 trials), GZ had the largest completion count (23/24; Fixed: 20/24; F+C: 21/24), whereas F+C was preferred by 6/8 participants. Therefore, completion and preference favored different mappings in this sample.

arXiv:2509.22407v3 Announce Type: replace-cross Abstract: The generalization of vision-language-action (VLA) models heavily relies on diverse training data. However, acquiring large-scale robot manipulation data across varied object appearances is costly and labor-intensive. To address this, we introduce Embodied Manipulation Media Adaptation (EMMA), a framework for augmenting VLA policies that combines a generative data engine with an effective training pipeline. We introduce DreamTransfer, a diffusion Transformer-based architecture for generating multi-view consistent and geometrically plausible embodied manipulation videos. It supports text-driven video editing on foreground, background and lighting while preserving 3D structure and geometric fidelity. We also utilize a hybrid training set of real and generated data and propose AdaMix to enhance the training process. AdaMix is a training strategy that adaptively weights samples according to policy performance to emphasize challengin

arXiv:1808.10654v2 Announce Type: replace-cross Abstract: Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given rise to learning-in-simulation which consequently casts a question on whether the results transfer to real-world. In this paper, we are concerned with the problem of developing real-world perception for active agents, propose Gibson Virtual Environment for this purpose, and showcase sample perceptual tasks learned therein. Gibson is based on virtualizing real spaces, rather than using artificially designed ones, and currently includes over 1400 floor spaces from 572 full buildings. The main characteristics of Gibson are: I. being from the real-world and reflecting its semantic complexity, II. having an internal synthesis mechanism, "Goggles", enabling deploying the trained models in re

arXiv:2606.10180v2 Announce Type: replace Abstract: We introduce flow control of vision-language-action (VLA) models, a simple and effective way to steer VLA actions in real time through generic inputs, such as a keyboard. This method can be used out-of-the-box and does not require retraining or fine-tuning VLAs. It enables relatively crude user inputs to steer a VLA to align with user intent. Flow control writes the user input into the initial condition of the VLA's flow matching action expert. Because the flow is deterministic, integration retains information about the user input while moving it toward the action distribution learned during training, so that the generated actions reflect the user's intent (high fidelity) and stay close to the VLA's own unsteered actions (high conformity). We demonstrate that flow control has many desirable properties: (1) flow control accurately and responsively steers robot actions with user inputs, (2) it is robust to suboptimal user inputs, (3) it

arXiv:2605.19294v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) policies increasingly rely on asynchronous inference to hide model latency behind ongoing robot motion. During inference, the robot continues executing its current action chunk while the next chunk is generated from the observation available at inference start. By the time the new chunk is executed, the robot or scene may have changed, so an action that fits the prediction-time state can be misaligned with the execution-time state. Training-time methods modify the policy input or supervised target based on the expected execution delay, but do not directly supervise how actions should change as the scene evolves between prediction and execution. Runtime repair methods improve chunk continuity or modify actions during deployment, but can still leave actions aligned with the prediction-time state rather than the execution-time state. Our key insight is that execution-time observations are unavailable when the

arXiv:2603.16536v2 Announce Type: replace Abstract: We present Kamino, a GPU-based physics solver for massively parallel simulations of heterogeneous highly-coupled mechanical systems. Implemented in Python using NVIDIA Warp and integrated into the Newton framework, it enables the application of data-driven methods, such as large-scale reinforcement learning, to complex robotic systems that exhibit strongly coupled kinematic and dynamic constraints such as kinematic loops. The latter are often circumvented by approximating the system's topology as a kinematic tree, and incorporating explicit loop-closure constraints or so-called mimic joints. Kamino aims at alleviating this burden by natively supporting these types of couplings. Moreover, Kamino supports heterogeneous worlds, allowing for batched simulation of structurally diverse robots on a single GPU. At its core lies a state-of-the-art constrained optimization algorithm based on ADMM, which computes constraint forces by solving the

arXiv:2410.03246v3 Announce Type: replace Abstract: Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algorithms, the resulting solutions are often brittle and appear unnatural. This is especially true for learning direct joint-level torque control, as inductive biases are difficult to integrate into the learning process. We propose an inductive bias for learning locomotion that is especially useful for torque control: latent actions learned from a small dataset of expert demonstrations. This prior allows the policy to directly leverage knowledge contained in the expert's actions and facilitates more efficient exploration. We observe that the agent is not restricted to the reward levels of the demonstration, and performance in transfer tasks is improved significantly. Latent action priors combined with style rewards for imitation lead to a closer replication of th

arXiv:2610.28530v1 Announce Type: cross Abstract: Collaborative perception enables agents to retrieve the scene semantics and geometry beyond the field of view of any individual agent. This broader scene understanding enhances transportation safety and improves efficiency in multi-robot systems. However, existing collaborative semantic occupancy methods rely on dense 3D voxel annotations and are limited to recognizing objects within a predefined set of semantic classes. To overcome these limitations, we present CoGaussian, an open-vocabulary Gaussian-based collaborative semantic occupancy prediction framework. CoGaussian represents a scene as a set of foundation model-aligned sparse 3D Gaussians, which serve as a compact representation for communicating and fusing geometric and semantic information across multiple agents. On the Semantic-OPV2V benchmark, our method outperforms existing open-vocabulary single-agent approaches, highlighting the effectiveness of open-vocabulary collaborat

arXiv:2610.27782v1 Announce Type: cross Abstract: Video generation models are promising foundations for embodied world models, but only if their generated rollouts are physically valid and faithful to task instructions. Multimodal large language models (MLLMs) are increasingly used to judge these properties, yet their agreement with human judgments on embodied videos has not been systematically evaluated. To address this gap, we introduce RoboJudge-Bench, a benchmark for evaluating MLLMs as judges of embodied video generation. The benchmark measures two fundamental dimensions, Physical Adherence and Instruction Alignment, across 800 human-annotated videos from 8 source corpora and 10 video generators, spanning grippers, human hands, and dexterous hands. Experiments reveal uneven alignment between existing judges and human ratings: general-purpose MLLMs show lower agreement on physical adherence, while specialized video judges designed for general-domain physical reasoning do not consis

arXiv:2610.27547v1 Announce Type: cross Abstract: Online LiDAR-camera calibration remains a fundamental yet challenging task in the field of robotics. Recently, deep learning-based data-driven approaches have emerged, offering high-precision, targetless calibration capabilities. However, existing methods often suffer from a critical dataset shortcut phenomenon due to the lack of extrinsic diversity in their training data. Networks tend to overfit to fixed nominal extrinsic configurations, leading to severe performance degradation and poor generalization in real-world deployments. To address this limitation, we propose a highly generalizable data generation framework tailored for training LiDAR-camera calibration networks. Our framework leverages existing omnidirectional image datasets annotated with depth ground truth to synthesize multi-modal calibration data under rigorous extrinsic constraints. Extensive experiments demonstrate that the data generated by our pipeline significantly e