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1. Robot Action Selection Learning via Layered Dimension Informed Program Synthesis

   Holtz, Jarrett ; Guha, Arjun ; Biswas, Joydeep ( November 2020 , Conference on Robot Learning (CoRL))

   null (Ed.)

   Action selection policies (ASPs), used to compose low-level robot skills into complex high-level tasks are commonly represented as neural networks (NNs) in the state of the art. Such a paradigm, while very effective, suffers from a few key problems: 1) NNs are opaque to the user and hence not amenable to verification, 2) they require significant amounts of training data, and 3) they are hard to repair when the domain changes. We present two key insights about ASPs for robotics. First, ASPs need to reason about physically meaningful quantities derived from the state of the world, and second, there exists a layered structure for composing these policies. Leveraging these insights, we introduce layered dimension-informed program synthesis (LDIPS) - by reasoning about the physical dimensions of state variables, and dimensional constraints on operators, LDIPS directly synthesizes ASPs in a human-interpretable domain-specific language that is amenable to program repair. We present empirical results to demonstrate that LDIPS 1) can synthesize effective ASPs for robot soccer and autonomous driving domains, 2) requires two orders of magnitude fewer training examples than a comparable NN representation, and 3) can repair the synthesized ASPs with only a small number of corrections when transferring from simulation to real robots. 

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2. Iterative Program Synthesis for Adaptable Social Navigation

   https://doi.org/10.1109/IROS51168.2021.9636540  

   Holtz, Jarrett ; Andrews, Simon ; Guha, Arjun ; Biswas, Joydeep ( September 2021 , 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Robot social navigation is influenced by human preferences and environment-specific scenarios such as elevators and doors, thus necessitating end-user adaptability. State-of-the-art approaches to social navigation fall into two categories: model-based social constraints and learning-based approaches. While effective, these approaches have fundamental limitations – model-based approaches require constraint and parameter tuning to adapt to preferences and new scenarios, while learning-based approaches require reward functions, significant training data, and are hard to adapt to new social scenarios or new domains with limited demonstrations.In this work, we propose Iterative Dimension Informed Program Synthesis (IDIPS) to address these limitations by learning and adapting social navigation in the form of human-readable symbolic programs. IDIPS works by combining pro-gram synthesis, parameter optimization, predicate repair, and iterative human demonstration to learn and adapt model-free action selection policies from orders of magnitude less data than learning-based approaches. We introduce a novel predicate repair technique that can accommodate previously unseen social scenarios or preferences by growing existing policies.We present experimental results showing that IDIPS: 1) synthesizes effective policies that model user preference, 2) can adapt existing policies to changing preferences, 3) can extend policies to handle novel social scenarios such as locked doors, and 4) generates policies that can be transferred from simulation to real-world robots with minimal effort. 

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3. Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis

   Tseng, Albert' Sun ; Yue, Yisong ( January 2022 , CVPR 2022)

   Obtaining annotations for large training sets is expen- sive, especially in settings where domain knowledge is re- quired, such as behavior analysis. Weak supervision has been studied to reduce annotation costs by using weak la- bels from task-specific labeling functions (LFs) to augment ground truth labels. However, domain experts still need to hand-craft different LFs for different tasks, limiting scal- ability. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level LFs. The key to our approach is to efficiently represent expert knowledge in a reusable domain-specific language and more general domain-level LFs, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate task-level LFs. Addition- ally, we propose a novel structural diversity cost that allows for efficient synthesis of diverse sets of LFs, further improv- ing AutoSWAP’s performance. We evaluate AutoSWAP in three behavior analysis domains and demonstrate that Au- toSWAP outperforms existing approaches using only a frac- tion of the data. Our results suggest that AutoSWAP is an effective way to automatically generate LFs that can signif- icantly reduce expert effort for behavior analysis. 

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4. Imitation-Projected Programmatic Reinforcement Learning

   Verma, A ( December 2019 , Advances in neural information processing systems)

   Wallach, H (Ed.)

   We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for such policies remains a challenge. Our approach to this challenge-a meta-algorithm called PROPEL-is based on three insights. First, we view our learning task as optimization in policy space, modulo the constraint that the desired policy has a programmatic representation, and solve this optimization problem using a form of mirror descent that takes a gradient step into the unconstrained policy space and then projects back onto the constrained space. Second, we view the unconstrained policy space as mixing neural and programmatic representations, which enables employing state-of-the-art deep policy gradient approaches. Third, we cast the projection step as program synthesis via imitation learning, and exploit contemporary combinatorial methods for this task. We present theoretical convergence results for PROPEL and empirically evaluate the approach in three continuous control domains. The experiments show that PROPEL can significantly outperform state-of-the-art approaches for learning programmatic policies. 

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5. Diffusion policy: Visuomotor policy learning via action diffusion

   https://doi.org/10.1177/02783649241273668  

   Chi, Cheng ; Xu, Zhenjia ; Feng, Siyuan ; Cousineau, Eric ; Du, Yilun ; Burchfiel, Benjamin ; Tedrake, Russ ; Song, Shuran ( October 2024 , The International Journal of Robotics Research)

   This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details are available (diffusion-policy.cs.columbia.edu).

    

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