13 accepted papers
Current approaches to evaluating personality in large language models (LLMs) typically prompt them to self-report on psychological questionnaires such as the Big Five Inventory. However, these methods assess introspective labels rather than observable behavior, despite the fact that LLMs are deploye
Safety-aware shared autonomy aims to enable an autonomous agent to collaborate with a human operator, completing tasks under safety constraints while maximally preserving human intent. However, existing methods often underperform in long-horizon tasks that require balancing task performance under sa
Reinforcement learning has shown strong promise for quadrupedal agile locomotion, even with proprioception-only sensing. In practice, however, sim-to-real gap and reward overfitting in complex terrains can produce policies that fail to transfer, while physical validation remains risky and inefficien…
Reinforcement learning from demonstrations (RLfD) offers a promising method for robotic manipulation with sparse rewards. However, limited demonstrations often cause agents to encounter out-of-distribution states where world models produce poor predictions. In multi-stage tasks, jointly optimizing a…
Large language models (LLMs) exhibit remarkable text-generation capabilities, yet struggle with factual consistency, motivating growing interest in factuality verification. Existing factuality verification methods typically follow a Decompose-Then-Verify paradigm, which improves granularity but suff
In reinforcement learning (RL) based robot skill acquisition, a high-fidelity simulator is usually indispensable but unattainable since the real environment dynamics are difficult to model, which leads to severe sim-to-real gaps. Existing methods solve this problem by combining offline and online RL
Grasping large and flat objects (e.g., a book or a pan) is often regarded as an ungraspable task, which poses significant challenges due to the unreachable grasping poses. Prior research has exploited environmental interactions through Extrinsic Dexterity, utilizing external structures such as walls
Recent progress in generative models has stimulated significant innovations in many fields, such as image generation and chatbots. Despite their success, these models often produce sketchy and misleading solutions for complex multi-agent decision-making problems because they miss the trial-and-error…
Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learning for decomposing joint action spaces instead of…
Sharing intentions is crucial for efficient cooperation in communication-enabled multi-agent reinforcement learning. Recent work applies static or undirected graphs to determine the order of interaction. However, the static graph is not general for complex cooperative tasks, and the parallel message…
Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decomposition (MVD) suffer from relative overgeneralization. As a result, they can not ensure optimal consistency (i.e., the corresp…
Constructing 3D cell module with certain shape and high cell density is a very important issue for artificial tissue engineering. In this letter, we present a novel method of fabricating compact microtissue with gear-like shape by electrodepositing of Ca <sup xmlns:mml="http://www.w3.org/1998/Math/M
In this study, cell-laden alginate-poly-L-lysine (PLL) hydrogel structures with arbitrary shapes were constructed based on electrodeposition method. Electrolysis of water in alginate solutions with calcium carbonate particles induced alginate gelation on micro-patterned anode electrode, and cell-lad…