VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training

Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging. We identify two critical mismatches: wrist-mounted fisheye views, with severe radial distortion and local gripper-centric perspectives, are out-of-distribution for pretrained VLMs; and human-collected trajectories frequently violate kinematic limits, incur collisions, or exceed controller bandwidth, teaching VLA policies physically infeasible actions. To address the challenges, we present VISTA, a framework that bridges this dual gap through three synergistic components. (i)~UMI-VQA, the first large-scale VQA dataset tailored to wrist-mounted fisheye observations, aligns VLM representations to the distorted visual regime via auxiliary vision-language supervision. (ii)~A systematic physical-validation pipeline performs a data-completeness pre-check and scores each valid trajectory for trajectory continuity, self-collision risk, and execution fidelity before it enters training. (iii)~A two-stage co-training recipe jointly learns vision-language grounding on UMI-VQA and action prediction on validated trajectories. Our experiments empirically show that incorporating UMI-VQA consistently improves downstream policy performance, and that physical-validation scores are strongly predictive of deployment success. On diverse simulation and real-world manipulation tasks, VISTA significantly outperforms strong baselines including $π_{0.5}$, LingBot-VLA, and Wall-X. We release the physical-validation pipeline, UMI-VQA, validated trajectory data, and the pre-trained model for the community.

AssemLM: Spatial Reasoning Multimodal Large Language Models for Robotic Assembly

Spatial reasoning is a fundamental capability for embodied intelligence, especially for fine-grained manipulation tasks such as robotic assembly. While recent vision-language models (VLMs) exhibit preliminary spatial awareness, they largely rely on coarse 2D perception and lack the ability to perform accurate reasoning over 3D geometry, which is crucial for precise assembly operations. To address this limitation, we propose AssemLM, a spatial multimodal large language model tailored for robotic assembly. AssemLM integrates assembly manuals, point clouds, and textual instructions to reason about and predict task-critical 6D assembly poses, enabling explicit geometric understanding throughout the assembly process. To effectively bridge raw 3D perception and high-level reasoning, we adopt a specialized point cloud encoder to capture fine-grained geometric and rotational features, which are then integrated into the multimodal language model to support accurate 3D spatial reasoning for assembly tasks. In addition, we construct AssemBench, a large-scale dataset and benchmark for assembly-oriented spatial reasoning, comprising over 900K multimodal samples with precise 6D pose annotations. AssemBench extends spatial reasoning evaluation beyond 2D and grounding tasks into full 3D geometric inference, filling a critical gap in existing embodied AI benchmarks. Extensive experiments demonstrate that AssemLM achieves state-of-the-art performance in 6D pose reasoning across diverse assembly scenarios. Furthermore, real-robot evaluations show that our model can support fine-grained and multi-step assembly execution in real-world settings, demonstrating its potential for robotic assembly applications.

Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning

Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches that mimic whole-body motions often neglect the distinct roles of upper and lower body. This oversight leads to computationally intensive policy learning and frequently causes robot instability and falls during real-world execution. To address these issues, we propose Adversarial Locomotion and Motion Imitation (ALMI), a novel framework that enables adversarial policy learning between upper and lower body. Specifically, the lower body aims to provide robust locomotion capabilities to follow velocity commands while the upper body tracks various motions. Conversely, the upper-body policy ensures effective motion tracking when the robot executes velocity-based movements. Through iterative updates, these policies achieve coordinated whole-body control, which can be extended to loco-manipulation tasks with teleoperation systems. Extensive experiments demonstrate that our method achieves robust locomotion and precise motion tracking in both simulation and on the full-size Unitree H1 robot. Additionally, we release a large-scale whole-body motion control dataset featuring high-quality episodic trajectories from MuJoCo simulations deployable on real robots. The project page is https://almi-humanoid.github.io.