Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
Ben Eisner, Yi Yang, Todor Davchev, Mel Vecerik, Jonathan Scholz,, David Held
TL;DR
This paper introduces a provably SE(3)-equivariant method for precise geometric reasoning in robotic placement tasks, enabling accurate object positioning with minimal demonstrations and robust generalization.
Contribution
The work presents a novel SE(3)-equivariant learning framework that separates invariant scene representation from equivariant reasoning layers, improving placement accuracy and data efficiency.
Findings
Outperforms previous methods in simulated placement accuracy
Accurately models relative placement relationships from real-world data
Requires only a few demonstrations for effective learning
Abstract
Many robot manipulation tasks can be framed as geometric reasoning tasks, where an agent must be able to precisely manipulate an object into a position that satisfies the task from a set of initial conditions. Often, task success is defined based on the relationship between two objects - for instance, hanging a mug on a rack. In such cases, the solution should be equivariant to the initial position of the objects as well as the agent, and invariant to the pose of the camera. This poses a challenge for learning systems which attempt to solve this task by learning directly from high-dimensional demonstrations: the agent must learn to be both equivariant as well as precise, which can be challenging without any inductive biases about the problem. In this work, we propose a method for precise relative pose prediction which is provably SE(3)-equivariant, can be learned from only a few…
Peer Reviews
Decision·ICLR 2024 poster
The paper's originality lies in its novel representation for cross-object relationships and the formulation of a problem-solving approach that is SE(3)-Equivariant. The quality of the work is high, evidenced by the clear methodology and promising experimental results. The clarity of the presentation is commendable, with complex concepts and processes being explained with precision. The experiment results are supportive to the precision requirement of the tasks and explained by the algorithm desi
Although the experimental results are positive and supportive to the claims on e.g., precision and the algorithm design, more tasks or scenarios could be evaluated. They can still be precise pick and place but should at least be different sets of objects, such as peg-in-hole. The methodology looks pretty promising (equivariant + differentiable optimization process) and generic, as a submission to a ML conference, one would expect to see a more diverse evaluation of the approach. Other weakness
- This paper proposes a method that tackles SE(3)-equivariant learning by estimating the corresponding point pairs with differentiable multilateration. The method provides the community with some fresh new ideas. - This paper is in general written in a clear way, and is easy to comprehend.
- Presentation - The problem statement section as well as some figures are directly borrowed from TAX-Pose. I think it severely damages the presentation of this paper. - Performance - The result in Table 1 is margianl improvement compared with PAX-Pose, though the proposed method does exhibit some advantages in higher-precision settings. - Real-world experiments - This method is evaluated with offline real-world trajectories collected by TAX-Pose. I won't accept this as real-world ex
1. The idea of using multilateration to calculate the relative pose is a compelling aspect of this paper. It transforms the equivariant problem of calculating the desired relative pose into an invariant problem of calculating the desired relative distance, which could be useful to reduce the complexity of a model. 2. The paper is well-written with intuitive examples to illustrate the idea.
My main concern with the paper is that the experimental evaluation is not strong enough. In the main paper, the experiments are mainly conducted in the Mug Hanging domain. In the two other domains in Table 3 in the appendix, the proposed method’s performance is worse than the baselines. Though the authors discuss that the underperformance compared with TAX-Pose could be due to the lack of the implementation of the symmetry-breaking technique, the proposed method also lags behind NDF. Additionall
Code & Models
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Taxonomy
TopicsWeb Applications and Data Management
MethodsSparse Evolutionary Training