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Atharv Sonwane

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3 papers

2 author rows

ICRA Conference 2023 Conference Paper

Policy-Guided Lazy Search with Feedback for Task and Motion Planning

Mohamed Khodeir
Atharv Sonwane
Ruthrash Hari
Florian Shkurti

PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems. Code can be found at https://rvl.cs.toronto.edu/learning-based-tamp.

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NeSy Conference 2022 Conference Paper

Neural Analogical Reasoning

Atharv Sonwane
Abhinav Lalwani
Sweta Mahajan
Gautam Shroff
Lovekesh Vig

Symbolic systems excel at reusing and composing modular functional units when solving problems such as simple analogical reasoning. However, they are less amenable to processing real-world data (e. g. images), and rely on additional (often hard-coded) mechanisms to convert such high-dimensional data to symbolic descriptions. In this work, we describe a modular approach ‘Neural Analogical Reasoning’ wherein elementary neural transformations operate and compose on distributed representations of high-dimensional inputs. We apply this approach on a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input-output images are related, so as to analogously transform future inputs. This can be viewed as a program synthesis task and solved via symbolic search if represented in symbolic form. Instead, we search for a sequence of elementary neural network transformations that manipulate distributed representations of the inputs. We present two variations of learning useful representations for this task and compare both with end-to-end meta-learning based approaches to demonstrate the importance of performing an explicit search.

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AAAI Conference 2022 Short Paper

Solving Visual Analogies Using Neural Algorithmic Reasoning (Student Abstract)

Atharv Sonwane
Gautam Shroff
Lovekesh Vig
Ashwin Srinivasan
Tirtharaj Dash

We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs. This program synthesis task can be easily solved via symbolic search. Using a variation of the ‘neural analogical reasoning’ approach, we instead search for a sequence of elementary neural network transformations that manipulate distributed representations derived from a symbolic space, to which input images are directly encoded. We evaluate the extent to which our ‘neural reasoning’ approach generalises for images with unseen shapes and positions.

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