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RISE: An Efficient 3D Perception-Based Policy for Real-World Robot Manipulation


핵심 개념
RISE, an end-to-end baseline for real-world robot imitation learning, leverages 3D perception to predict continuous robot actions directly from single-view point clouds, demonstrating significant advantages in accuracy and efficiency compared to existing 2D and 3D policies.
요약
The paper presents RISE, an end-to-end baseline for real-world robot imitation learning that utilizes 3D perception to predict continuous robot actions. The key highlights are: RISE takes a noisy single-view point cloud as input and outputs continuous robot actions through an efficient pipeline. The pipeline consists of a sparse 3D encoder to compress the point cloud into tokens, a transformer to map the tokens to action features, and a diffusion-based decoder to generate continuous actions. Sparse positional encoding is introduced to effectively capture the 3D spatial relationships among the unordered point tokens. Evaluated on 6 real-world tasks, RISE significantly outperforms representative 2D and 3D baselines in both accuracy and efficiency, showcasing strong generalization abilities to various environmental disturbances. Ablation studies demonstrate the effectiveness of 3D perception and the advantages of RISE over keyframe-based 3D policies and image-based policies with 3D encoders.
통계
"Trained with 50 demonstrations for each real-world task, RISE surpasses currently representative 2D and 3D policies by a large margin, showcasing significant advantages in both accuracy and efficiency." "RISE exhibits strong generalization abilities across all levels of testing, even in the most challenging L4-level tests involving changes in the camera view."
인용문
"RISE significantly outperforms representative 2D and 3D policies in multiple tasks, demonstrating great advantages in both accuracy and efficiency." "Ablation studies demonstrate the effectiveness of 3D perception and the advantages of RISE over keyframe-based 3D policies and image-based policies with 3D encoders."

에서 추출된 주요 통찰력

by Chenxi Wang,... 위치 arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12281.pdf
RISE: 3D Perception Makes Real-World Robot Imitation Simple and  Effective

심층적인 질문

How can RISE's 3D perception module be further improved to enhance its robustness and generalization capabilities?

RISE's 3D perception module can be enhanced in several ways to improve its robustness and generalization capabilities. One approach could be to incorporate multi-view information from additional cameras to provide a more comprehensive understanding of the environment. By fusing data from multiple viewpoints, the model can better capture the spatial relationships between objects and improve its ability to generalize across different camera angles. Another improvement could involve integrating self-supervised learning techniques to enhance the model's ability to learn from unlabeled data. By leveraging self-supervised learning tasks such as depth prediction or view synthesis, the model can gain a better understanding of the 3D structure of the scene and improve its generalization to novel environments. Furthermore, incorporating attention mechanisms into the 3D perception module can help the model focus on relevant parts of the point cloud, improving its efficiency and robustness. Attention mechanisms can enable the model to dynamically adjust its focus based on the context of the task, leading to more accurate and generalizable representations of the environment.

What are the potential limitations of RISE's diffusion-based action decoder, and how could alternative decoding approaches be explored to address them?

While the diffusion-based action decoder in RISE has shown effectiveness in generating diverse trajectories, it may have limitations in handling complex action spaces or long-horizon tasks. One potential limitation is the computational complexity of the diffusion process, which can increase significantly with larger action spaces or longer prediction horizons. This could lead to slower inference times and scalability issues in real-world applications. To address these limitations, alternative decoding approaches could be explored. One approach could be to incorporate reinforcement learning techniques to learn a policy directly from the encoded features, bypassing the diffusion process. Reinforcement learning algorithms such as Proximal Policy Optimization (PPO) or Deep Deterministic Policy Gradient (DDPG) could be used to train the policy end-to-end, allowing for more efficient and scalable action generation. Another approach could involve using a recurrent neural network (RNN) or transformer-based architecture for sequence prediction. By modeling the action generation as a sequential process, the model can capture temporal dependencies in the data and generate more accurate and coherent action trajectories. This approach could be particularly beneficial for tasks that require long-horizon predictions or complex action sequences.

Given the success of RISE in real-world robot manipulation, how could the insights from this work be applied to other robotic domains, such as navigation or legged locomotion, that also rely heavily on spatial perception?

The insights from RISE's success in real-world robot manipulation can be applied to other robotic domains that rely on spatial perception, such as navigation or legged locomotion, in the following ways: Spatial Perception in Navigation: RISE's 3D perception module can be adapted for use in navigation tasks that require understanding the spatial layout of the environment. By incorporating 3D point cloud data from sensors such as LiDAR or depth cameras, the model can generate more accurate representations of the surroundings and improve navigation performance in complex environments. Legged Locomotion: For legged locomotion tasks, the robustness and generalization capabilities of RISE's policy learning framework can be leveraged to train controllers for legged robots. By integrating 3D perception with dynamic control algorithms, the model can learn to adapt to varying terrains and obstacles, enhancing the robot's agility and stability during locomotion. Transfer Learning: The transfer learning techniques used in RISE can be applied to transfer knowledge and skills learned in one robotic domain to another. By fine-tuning the pre-trained models on specific tasks in navigation or legged locomotion, robots can quickly adapt to new environments and tasks, reducing the need for extensive retraining. Overall, the insights from RISE can pave the way for advancements in spatial perception and control in various robotic domains, enabling robots to navigate autonomously and perform complex locomotion tasks with efficiency and accuracy.
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