7 accepted papers
Existing tasks fall short in evaluating reasoning ability of Large Language Models (LLMs) in an interactive, unknown environment. This deficiency leads to the isolated assessment of deductive, inductive, and abductive reasoning, neglecting the integrated reasoning process that is indispensable for h…
Lightweight Large Language Models (LwLLMs) are reduced-parameter, optimized models designed to run efficiently on consumer-grade hardware, offering significant advantages in resource efficiency, cost-effectiveness, and data privacy. However, these models often struggle with limited inference and rea…
The clinical diagnosis of most mental disorders primarily relies on the conversations between psychiatrist and patient. The creation of such diagnostic conversation datasets is promising to boost the AI mental healthcare community. However, directly collecting the conversations in real diagnosis sce…
Recent planning methods based on Large Language Models typically employ the In-Context Learning paradigm. Complex long-horizon planning tasks require more context(including instructions and demonstrations) to guarantee that the generated plan can be executed correctly. However, in such conditions, L…
Multi-task learning has expanded the boundaries of robotic manipulation, enabling the execution of increasingly complex tasks. However, policies learned through reinforcement learning exhibit limited generalization and narrow distributions, which restrict their effectiveness in multi-task training.…
In this letter, we focus on online motion planning for manipulators in dynamic obstacle environments. An analytical geometry-based inverse kinematics solution for generalized types of 7-DoF anthropomorphic manipulators is presented to work as the basis of high-efficiency collision avoidance planning
In this paper, we present a framework to generate time-optimal trajectories for dynamic target pick-and-place tasks. We develop an optimization-based trajectory generation method for manipulators, which can conduct spatial-temporal deformation under user-defined requirements. We formulate the proble…