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Boshi An

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3 papers

2 author rows

AAAI Conference 2024 Conference Paper

Learning Multi-Object Positional Relationships via Emergent Communication

Yicheng Feng
Boshi An
Zongqing Lu

The study of emergent communication has been dedicated to interactive artificial intelligence. While existing work focuses on communication about single objects or complex image scenes, we argue that communicating relationships between multiple objects is important in more realistic tasks, but understudied. In this paper, we try to fill this gap and focus on emergent communication about positional relationships between two objects. We train agents in the referential game where observations contain two objects, and find that generalization is the major problem when the positional relationship is involved. The key factor affecting the generalization ability of the emergent language is the input variation between Speaker and Listener, which is realized by a random image generator in our work. Further, we find that the learned language can generalize well in a new multi-step MDP task where the positional relationship describes the goal, and performs better than raw-pixel images as well as pre-trained image features, verifying the strong generalization ability of discrete sequences. We also show that language transfer from the referential game performs better in the new task than learning language directly in this task, implying the potential benefits of pre-training in referential games. All in all, our experiments demonstrate the viability and merit of having agents learn to communicate positional relationships between multiple objects through emergent communication.

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ICRA Conference 2024 Conference Paper

RGBManip: Monocular Image-based Robotic Manipulation through Active Object Pose Estimation

Boshi An
Yiran Geng
Kai Chen 0028
Xiaoqi Li 0020
Qi Dou 0001
Hao Dong 0003

Robotic manipulation requires accurate perception of the environment, which poses a significant challenge due to its inherent complexity and constantly changing nature. In this context, RGB image and point-cloud observations are two commonly used modalities in visual-based robotic manipulation, but each of these modalities have their own limitations. Commercial point-cloud observations often suffer from issues like sparse sampling and noisy output due to the limits of the emission-reception imaging principle. On the other hand, RGB images, while rich in texture information, lack essential depth and 3D information crucial for robotic manipulation. To mitigate these challenges, we propose an image-only robotic manipulation framework that leverages an eye-on-hand monocular camera installed on the robot’s parallel gripper. By moving with the robot gripper, this camera gains the ability to actively perceive the object from multiple perspectives during the manipulation process. This enables the estimation of 6D object poses, which can be utilized for manipulation. While, obtaining images from more and diverse viewpoints typically improves pose estimation, it also increases the manipulation time. To address this trade-off, we employ a reinforcement learning policy to synchronize the manipulation strategy with active perception, achieving a balance between 6D pose accuracy and manipulation efficiency. Our experimental results in both simulated and real-world environments showcase the state-of-the-art effectiveness of our approach. We believe that our method will inspire further research on real-world-oriented robotic manipulation. See https://rgbmanip.github.io/for more details.

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ICRA Conference 2023 Conference Paper

RLAfford: End-to-End Affordance Learning for Robotic Manipulation

Yiran Geng
Boshi An
Haoran Geng
Yuanpei Chen
Yaodong Yang 0001
Hao Dong 0003

Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can generalize over objects with different semantic categories, diverse shape geometry and versatile functionality. In this study, we focused on the contact information in manipulation processes, and proposed a unified representation for critical interactions to describe different kinds of manipulation tasks. Specifically, we take advantage of the contact information generated during the RL training process and employ it as unified visual representation to predict contact map of interest. Such representation leads to an end-to-end learning framework that combined affordance based and RL based methods for the first time. Our unified framework can generalize over different types of manipulation tasks. Surprisingly, the effectiveness of such framework holds even under the multi-stage and multi-agent scenarios. We tested our method on eight types of manipulation tasks. Results showed that our methods outperform baseline algorithms, including visual affordance methods and RL methods, by a large margin on the success rate. The demonstration can be found at https://sites.google.com/view/rlafford/.

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