Memory-Consistent Neural Networks for Imitation Learning
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, James Weimer,, Insup Lee
TL;DR
This paper introduces memory-consistent neural networks (MCNNs) for imitation learning, which constrain policy outputs within permissible regions to reduce errors and improve performance across diverse tasks.
Contribution
The authors propose MCNNs that enforce output constraints based on memory samples, providing theoretical guarantees and demonstrating superior performance over standard neural networks in imitation learning.
Findings
MCNNs outperform vanilla neural networks in imitation tasks.
The approach provides a theoretical upper bound on sub-optimality.
Validated across diverse tasks with different architectures and inputs.
Abstract
Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations. For such imitation policies, errors away from the training samples are particularly critical. Even rare slip-ups in the policy action outputs can compound quickly over time, since they lead to unfamiliar future states where the policy is still more likely to err, eventually causing task failures. We revisit simple supervised ``behavior cloning'' for conveniently training the policy from nothing more than pre-recorded demonstrations, but carefully design the model class to counter the compounding error phenomenon. Our ``memory-consistent neural network'' (MCNN) outputs are hard-constrained to stay within clearly specified permissible regions anchored to prototypical ``memory'' training samples. We provide a guaranteed upper bound for the…
Peer Reviews
Decision·ICLR 2024 poster
* Constraining the output of the neural network such that it stays "close" to a specified set of data points is an interesting approach for tackling behavior cloning. Even more so since this can be combined with underlying network architectures. * The empirical evaluation is fairly thorough, and shows reasonable performance gains across many of the tasks. * The implementation seems straightforward (although this will incur an additional computational cost during training and inference).
* As a (semi-)parametric method, the computational cost of training and inference scales with the number of memories. There is a discussion on computational complexity in the appendix, but some analysis on training time would be appreciated here as it's difficult to tell whether this is a significant factor. * The performance improvement seems sensitive to the underlying network architecture and the task. E.g. in Fig. 4, different models exhibit different levels of improvement for each task. Thi
The authors present all the material in a very easy to follow manner. All figures are good quality and the results are well displayed. The authors have performed extensive experimentation and comparison to the baselines. In addition, the baselines selected are reasonable as this work aims to improve the behavior of BC methods. Finally, they offer adequate implementation details in their appendix. Their method is predicated on a simple yet elegant idea: use expert demonstrations as a human-like
The main weakness of this work, as is common with BC approaches, is assumption of representative state-action pairs and optimal behavior provided by the demonstrator. There is no insight as to how the method will behave with reasonably suboptimal demonstrations, as it being a BC based approach has no apparent mechanism that enables it to focus on the better demonstrations of a provided set. There is recent interest in work that can discern between useful demonstrations and harmful / irrelevant
- The paper proposes a new class of functions that combines non-parametric nearest neighbor based policy learning with parametric neural network based policies. As a result of this amalgam, the authors show that the MCNN class of functions is bounded in width and the suboptimality gap, something that does not exist for vanilla neural networks. - The authors provide results across 5 environments where adding MCNN to a new architecture consistently improves the results. - The authors ablate the pe
- The authors mention that they provide results on 9 tasks across 5 environments. But I only see 5 tasks, 1 per environment. It would be great if the authors could clarify the 9 tasks that they evaluate on and where they have provided the results. - For CARLA, the images have been embedded using a fixed off-the-shelf ResNet34 encoder. This might not be ideal for more complicated visual scenes such as the Franka Kitchen environment used in BeT and Diffusion Policy. It would be great if the author
Videos
Taxonomy
TopicsReinforcement Learning in Robotics · Robot Manipulation and Learning · Human Pose and Action Recognition
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Position-Wise Feed-Forward Layer · Softmax · Byte Pair Encoding · Label Smoothing · Adam · Absolute Position Encodings · Residual Connection