UniBiDex: A Unified Teleoperation Framework for Robotic Bimanual Dexterous Manipulation

We present UniBiDex a unified teleoperation framework for robotic bimanual dexterous manipulation that supports both VRbased and leaderfollower input modalities UniBiDex enables realtime contactrich dualarm teleoperation by integrating heterogeneous input devices into a shared control stack with consistent kinematic treatment and safety guarantees The framework employs nullspace control to optimize bimanual configurations ensuring smooth collisionfree and singularityaware motion across tasks We validate UniBiDex on a longhorizon kitchentidying task involving five sequential manipulation subtasks demonstrating higher task success rates smoother trajectories and improved robustness compared to strong baselines By releasing all hardware and software components as opensource we aim to lower the barrier to collecting largescale highquality human demonstration datasets and accelerate progress in robot learning.

Blind Source Separation Based on Sparsity

Blind source separation (BSS) is a key technique in array processing and data analysis, aiming to recover unknown sources from observed mixtures without knowledge of the mixing matrix. Classical independent component analysis (ICA) methods rely on the assumption that sources are mutually independent. To address limitations of ICA, sparsity-based methods have been introduced, which decompose source signals sparsely in a predefined dictionary. Morphological Component Analysis (MCA), based on sparse representation theory, assumes that a signal is a linear combination of components with distinct geometries, each sparsely representable in one dictionary and not in others. This approach has recently been applied to BSS with promising results. This report reviews key approaches derived from classical ICA and explores sparsity-based methods for BSS. It introduces the theory of sparse representation and decomposition, followed by a block coordinate relaxation MCA algorithm, whose variants are used in Multichannel MCA (MMCA) and Generalized MCA (GMCA). A local dictionary learning method using K-SVD is then presented. Finally, we propose an improved algorithm, SAC+BK-SVD, which enhances K-SVD by learning a block-sparsifying dictionary that clusters and updates similar atoms in blocks. The implementation includes experiments on image segmentation and blind image source separation using the discussed techniques. We also compare the proposed block-sparse dictionary learning algorithm with K-SVD. Simulation results demonstrate that our method yields improved blind image separation quality.

PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models

We introduce PHYBench, a novel, high-quality benchmark designed for evaluating reasoning capabilities of large language models (LLMs) in physical contexts. PHYBench consists of 500 meticulously curated physics problems based on real-world physical scenarios, designed to assess the ability of models to understand and reason about realistic physical processes. Covering mechanics, electromagnetism, thermodynamics, optics, modern physics, and advanced physics, the benchmark spans difficulty levels from high school exercises to undergraduate problems and Physics Olympiad challenges. Additionally, we propose the Expression Edit Distance (EED) Score, a novel evaluation metric based on the edit distance between mathematical expressions, which effectively captures differences in model reasoning processes and results beyond traditional binary scoring methods. We evaluate various LLMs on PHYBench and compare their performance with human experts. Our results reveal that even state-of-the-art reasoning models significantly lag behind human experts, highlighting their limitations and the need for improvement in complex physical reasoning scenarios. Our benchmark results and dataset are publicly available at https://phybench-official.github.io/phybench-demo/.