Zobrazeno 1 - 10
of 4 147
pro vyhledávání: '"Zhu Song"'
Autor:
Yan, Sixu, Zhang, Zeyu, Han, Muzhi, Wang, Zaijin, Xie, Qi, Li, Zhitian, Li, Zhehan, Liu, Hangxin, Wang, Xinggang, Zhu, Song-Chun
Recent advances in diffusion models have opened new avenues for research into embodied AI agents and robotics. Despite significant achievements in complex robotic locomotion and skills, mobile manipulation-a capability that requires the coordination
Externí odkaz:
http://arxiv.org/abs/2410.11402
Large Language Models (LLMs) trained on massive corpora have shown remarkable success in knowledge-intensive tasks. Yet, most of them rely on pre-stored knowledge. Inducing new general knowledge from a specific environment and performing reasoning wi
Externí odkaz:
http://arxiv.org/abs/2410.08126
Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation. However, existing approaches often struggle to achieve both objectives. In this paper, based on that
Externí odkaz:
http://arxiv.org/abs/2410.07863
Autor:
Zhang, Zeyu, Yan, Sixu, Han, Muzhi, Wang, Zaijin, Wang, Xinggang, Zhu, Song-Chun, Liu, Hangxin
We propose M^3Bench, a new benchmark of whole-body motion generation for mobile manipulation tasks. Given a 3D scene context, M^3Bench requires an embodied agent to understand its configuration, environmental constraints and task objectives, then gen
Externí odkaz:
http://arxiv.org/abs/2410.06678
Autor:
Liu, Hangxin, Xie, Qi, Zhang, Zeyu, Yuan, Tao, Leng, Xiaokun, Sun, Lining, Zhu, Song-Chun, Zhang, Jingwen, He, Zhicheng, Su, Yao
This paper presents the development of a Physics-realistic and Photo-\underline{r}ealistic humanoid robot testbed, PR2, to facilitate collaborative research between Embodied Artificial Intelligence (Embodied AI) and robotics. PR2 offers high-quality
Externí odkaz:
http://arxiv.org/abs/2409.01559
Autor:
Li, Pengxiang, Gao, Zhi, Zhang, Bofei, Yuan, Tao, Wu, Yuwei, Harandi, Mehrtash, Jia, Yunde, Zhu, Song-Chun, Li, Qing
Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derive
Externí odkaz:
http://arxiv.org/abs/2407.11522
Recent advances in Large Language Models (LLMs) have shown inspiring achievements in constructing autonomous agents that rely on language descriptions as inputs. However, it remains unclear how well LLMs can function as few-shot or zero-shot embodied
Externí odkaz:
http://arxiv.org/abs/2406.16294
Despite the recent successes of multi-agent reinforcement learning (MARL) algorithms, efficiently adapting to co-players in mixed-motive environments remains a significant challenge. One feasible approach is to hierarchically model co-players' behavi
Externí odkaz:
http://arxiv.org/abs/2406.08002
Learning abstract state representations and knowledge is crucial for long-horizon robot planning. We present InterPreT, an LLM-powered framework for robots to learn symbolic predicates from language feedback of human non-experts during embodied inter
Externí odkaz:
http://arxiv.org/abs/2405.19758
Multi-agent systems (MAS) need to adaptively cope with dynamic environments, changing agent populations, and diverse tasks. However, most of the multi-agent systems cannot easily handle them, due to the complexity of the state and task space. The soc
Externí odkaz:
http://arxiv.org/abs/2405.01839