Zobrazeno 1 - 10
of 8 152
pro vyhledávání: '"Park, Hae"'
Autor:
Kim, Yubin, Park, Chanwoo, Jeong, Hyewon, Grau-Vilchez, Cristina, Chan, Yik Siu, Xu, Xuhai, McDuff, Daniel, Lee, Hyeonhoon, Breazeal, Cynthia, Park, Hae Won
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (LLMs) promise to streamline this process by synthesizing vast medical
Externí odkaz:
http://arxiv.org/abs/2411.00248
This work introduces a model-free reinforcement learning framework that enables various modes of motion (quadruped, tripod, or biped) and diverse tasks for legged robot locomotion. We employ a motion-style reward based on a relaxed logarithmic barrie
Externí odkaz:
http://arxiv.org/abs/2409.15780
Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories. While empathy is influenced by narrative content, intuitively, people respond to the way a story is told as well, t
Externí odkaz:
http://arxiv.org/abs/2405.17633
Autor:
Shen, Jocelyn, Kim, Yubin, Hulse, Mohit, Zulfikar, Wazeer, Alghowinem, Sharifa, Breazeal, Cynthia, Park, Hae Won
Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy datasets fall short in capturing the richness of empathy responses, often
Externí odkaz:
http://arxiv.org/abs/2405.15708
Autor:
Kim, Yubin, Park, Chanwoo, Jeong, Hyewon, Chan, Yik Siu, Xu, Xuhai, McDuff, Daniel, Lee, Hyeonhoon, Ghassemi, Marzyeh, Breazeal, Cynthia, Park, Hae Won
Foundation models are becoming valuable tools in medicine. Yet despite their promise, the best way to leverage Large Language Models (LLMs) in complex medical tasks remains an open question. We introduce a novel multi-agent framework, named Medical D
Externí odkaz:
http://arxiv.org/abs/2404.15155
We describe an approach for aligning an LLM-based dialogue agent based on global (i.e., dialogue-level) rewards, while also taking into account naturally-occurring multimodal signals. At a high level, our approach (dubbed GELI) learns a local, turn-l
Externí odkaz:
http://arxiv.org/abs/2403.11330
Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-linguistic data is crucial. This paper investigates the capacity of LLM
Externí odkaz:
http://arxiv.org/abs/2401.06866
Publikováno v:
Proceedings of the 2024 ACM/IEEE International Conference on Human - Robot Interaction (HRI24), March 11 - 14, 2024, Boulder, CO, USA
In this paper, we introduce a novel conceptual model for a robot's behavioral adaptation in its long-term interaction with humans, integrating dynamic robot role adaptation with principles of flow experience from psychology. This conceptualization in
Externí odkaz:
http://arxiv.org/abs/2401.02833
This paper presents a method for achieving high-speed running of a quadruped robot by considering the actuator torque-speed operating region in reinforcement learning. The physical properties and constraints of the actuator are included in the traini
Externí odkaz:
http://arxiv.org/abs/2312.17507
This paper presents a contact-implicit model predictive control (MPC) framework for the real-time discovery of multi-contact motions, without predefined contact mode sequences or foothold positions. This approach utilizes the contact-implicit differe
Externí odkaz:
http://arxiv.org/abs/2312.08961