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Chao Yang 0026

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6 papers

1 author row

ICML Conference 2025 Conference Paper

Emergent Response Planning in LLMs

Zhichen Dong
Zhanhui Zhou
Zhixuan Liu
Chao Yang 0026
Chaochao Lu

In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: $\textbf{their hidden representations encode future outputs beyond the next token}$. Through simple probing, we demonstrate that LLM prompt representations encode global attributes of their entire responses, including $\textit{structure attributes}$ (e. g. , response length, reasoning steps), $\textit{content attributes}$ (e. g. , character choices in storywriting, multiple-choice answers at the end of response), and $\textit{behavior attributes}$ (e. g. , answer confidence, factual consistency). In addition to identifying response planning, we explore how it scales with model size across tasks and how it evolves during generation. The findings that LLMs plan ahead for the future in their hidden representations suggest potential applications for improving transparency and generation control.

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ICAPS Conference 2024 Conference Paper

Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning

Zhaoxun Ju
Chao Yang 0026
Fuchun Sun 0001
Hongbo Wang
Yu Qiao 0001

Language-conditioned robot behavior plays a vital role in executing complex tasks by associating human commands or instructions with perception and actions. The ability to compose long-horizon tasks based on unconstrained language instructions necessitates the acquisition of a diverse set of general-purpose skills. However, acquiring inherent primitive skills in a coupled and long-horizon environment without external rewards or human supervision presents significant challenges. In this paper, we evaluate the relationship between skills and language instructions from a mathematical perspective, employing two forms of mutual information within the framework of language-conditioned policy learning. To maximize the mutual information between language and skills in an unsupervised manner, we propose an end-to-end imitation learning approach known as Language Conditioned Skill Discovery (LCSD). Specifically, we utilize vector quantization to learn discrete latent skills and leverage skill sequences of trajectories to reconstruct high-level semantic instructions. Through extensive experiments on language-conditioned robotic navigation and manipulation tasks, encompassing BabyAI, LORel, and Calvin, we demonstrate the superiority of our method over prior works. Our approach exhibits enhanced generalization capabilities towards unseen tasks, improved skill interpretability, and notably higher rates of task completion success.

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ICML Conference 2024 Conference Paper

RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis

Yao Mu 0001
Junting Chen
Qinglong Zhang
Shoufa Chen
Qiaojun Yu
Chongjian Ge
Runjian Chen
Zhixuan Liang

Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successes in applying multimodal large language models for high-level understanding, it remains challenging to translate these conceptual understandings into detailed robotic actions while achieving generalization across various scenarios. In this paper, we propose a tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX. RoboCodeX decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints, and applies code generation to introduce generalization ability across various robotics platforms. To further enhance the capability to map conceptual and perceptual understanding into control commands, a specialized multimodal reasoning dataset is collected for pre-training and an iterative self-updating methodology is introduced for supervised fine-tuning. Extensive experiments demonstrate that RoboCodeX achieves state-of-the-art performance in both simulators and real robots on four different kinds of manipulation tasks and one embodied navigation task.

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ICLR Conference 2023 Conference Paper

GoBigger: A Scalable Platform for Cooperative-Competitive Multi-Agent Interactive Simulation

Ming Zhang
Shenghan Zhang
Zhenjie Yang
Lekai Chen
Jinliang Zheng
Chao Yang 0026
Chuming Li
Hang Zhou 0009

The emergence of various multi-agent environments has motivated powerful algorithms to explore agents' cooperation or competition. Even though this has greatly promoted the development of multi-agent reinforcement learning (MARL), it is still not enough to support further exploration on the behavior of swarm intelligence between multiple teams, and cooperation between multiple agents due to their limited scalability. To alleviate this, we introduce GoBigger, a scalable platform for cooperative-competition multi-agent interactive simulation. GoBigger is an enhanced environment for the Agar-like game, enabling the simulation of multiple scales of agent intra-team cooperation and inter-team competition. Compared with existing multi-agent simulation environments, our platform supports multi-team games with more than two teams simultaneously, which dramatically expands the diversity of agent cooperation and competition, and can more effectively simulate the swarm intelligent agent behavior. Besides, in GoBigger, the cooperation between the agents in a team can lead to much higher performance. We offer a diverse set of challenging scenarios, built-in bots, and visualization tools for best practices in benchmarking. We evaluate several state-of-the-art algorithms on GoBigger and demonstrate the potential of the environment. We believe this platform can inspire various emerging research directions in MARL, swarm intelligence, and large-scale agent interactive learning. Both GoBigger and its related benchmark are open-sourced. More information could be found at https://github.com/opendilab/GoBigger.

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ICRA Conference 2021 Conference Paper

Adversarial Skill Learning for Robust Manipulation

Pingcheng Jian
Chao Yang 0026
Di Guo 0002
Huaping Liu 0001
Fuchun Sun 0001

Deep reinforcement learning has made significant progress in robotic manipulation tasks and it works well in the ideal disturbance-free environment. However, in a real-world environment, both internal and external disturbances are inevitable, thus the performance of the trained policy will dramatically drop. To improve the robustness of the policy, we introduce the adversarial training mechanism to the robotic manipulation tasks in this paper, and an adversarial skill learning algorithm based on soft actor-critic (SAC) is proposed for robust manipulation. Extensive experiments are conducted to demonstrate that the learned policy is robust to internal and external disturbances. Additionally, the proposed algorithm is evaluated in both the simulation environment and on the real robotic platform.

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ICRA Conference 2018 Conference Paper

A Dual-Modal Vision-Based Tactile Sensor for Robotic Hand Grasping

Bin Fang 0003
Fuchun Sun 0001
Chao Yang 0026
Hongxiang Xue
Wendan Chen
Chun Zhang
Di Guo 0002
Huaping Liu 0001

Humans' fingertips can perceive not only the magnitude and the direction of force but also the texture of object. When we grasp an object, the surface texture sensing of the fingertip helps us recognize the object and the force feeling that is parallel to the skin helps us grasp stably. Focusing on these points, we have developed a dual-modal vision-based tactile sensor that can measure the texture of object and a distribution of force vectors. The tactile sensor consists of a transparent elastomer, a camera, a piece of transparent acrylic board, LEDs and supporting structures. A reflective membrane and markers array are on the surface of the elastomer. An applied force on the elastic body results in movements of the markers, which are acquired by the CCD camera. In addition, the shape and texture of the object's contact surface can be reflected by the membrane deformations. The distribution of force vectors is determined by the BP neural network. The local binary pattern algorithm using captured images calculates the texture information. This paper reports experimental evaluation results concerning accuracy of determination of magnitude, direction of force, and texture recognition rate.

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