Tactile feedback provides embodied agents with physical information beyond visual observations, enabling more reliable interaction with the real world. However, despite the rapid progress of vision-tactile-language-action (VTLA) policies, there remains a lack of unified benchmarks for evaluating tactile-enabled robot m...
Natively training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. However, full attention incurs quadratic cost and, as resolution increases, spreads attention over increasingly redundant tokens, diluting learning signals for informativ...
Shu-Yuan Tu, Qi Tian, Yin-Ming Huang et al.· 0 citations
General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from digital environments to embodied manipulation. To investigate this question, we introduce LIBERO-Agent, an agent-native benchmark for evaluating these agents in robot mani...
Zi-Jie Diao, Yi-Tong Chen, Si-Cheng Xie et al.· 0 citations
VA-Judger is proposed, a chain-of-thought omni-reward model that learns from pairs with clear quality gaps to establish structured output and coarse preference discrimination, then distills reliable preference explanations for harder near-quality comparisons via rejection sampling verified against human annotations, an...
Yin-Ming Huang, Shu-Yuan Tu, Xi Yan et al.· 6 citations· ⚡1
A trajectory engine is built that reconstructs real livestream sessions into structured causal supervision, yielding over 320 hours of optimization trajectories and a human-reviewed benchmark of 275 clips and 13,812 decision intervals, establishing livestream assistance as selective participation in a shared social str...
Shu-Jian Gao, Jia-Mei Yan, Yu-Chen Yang et al.· 0 citations
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implausible actions. We propose the Large Discrete Policy (LDiP), a fully discrete behavior modeling framework that selects actions from a large vo...
Zhen-Xin Li, Nadine Chang, Xing-Long Sun et al.· 1 citation
Bench2Dex is meant as a platform for studying visuo-tactile learning across dexterous hands and offers a shared setting for algorithm development while tactile hardware and simulation models are still evolving.
Zhen-Jie Yang, Yi-Deng Zhang, Dong-Jie Zhang et al.· 1 citation
The Embodied Task Agent is introduced, a new paradigm for extending digital agents into the physical world, and OpenETA is released as its open-source implementation, which provides replaceable Planners, composable Tools and Skills, auditable memory, replayable trajectories, and common interfaces for simulation and rea...
Yi-Tong Chen, Zezheng Huai, Si-Xian Li et al.· 5 citations· ⚡1
CuteTTS is presented, a compact continuous-autoregressive TTS system that reconciles high-fidelity generation with the latency demands of real-time interaction and introduces guidance-step distillation, which absorbs classifier-free guidance and multiple solver steps into a single interval-conditioned student.
Yu-Qian Zhang, Yao Shi, Kexin Huang et al.· 0 citations
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