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Junchi Liang

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4 papers

2 author rows

ICRA Conference 2023 Conference Paper

Learning Category-Level Manipulation Tasks from Point Clouds with Dynamic Graph CNNs

Junchi Liang
Abdeslam Boularias

This paper presents a new technique for learning category-level manipulation from raw RGB-D videos of task demonstrations, with no manual labels or annotations. Category-level learning aims to acquire skills that can be generalized to new objects, with geometries and textures that are different from the ones of the objects used in the demonstrations. We address this problem by first viewing both grasping and manipulation as special cases of tool use, where a tool object is moved to a sequence of key-poses defined in a frame of reference of a target object. Tool and target objects, along with their key-poses, are predicted using a dynamic graph convolutional neural network that takes as input an automatically segmented depth and color image of the entire scene. Empirical results on object manipulation tasks with a real robotic arm show that the proposed network can efficiently learn from real visual demonstrations to perform the tasks on novel objects within the same category, and outperforms alternative approaches.

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ICRA Conference 2022 Conference Paper

Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations

Junchi Liang
Bowen Wen
Kostas E. Bekris
Abdeslam Boularias

This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performed by a person. The sub-tasks consist of moving the robot's end-effector until it reaches a sub-goal region in the task space, performing an action, and triggering the next sub-task when a pre-condition is met. Most prior work in this domain has been concerned with learning only low-level tasks, such as hitting a ball or reaching an object and grasping it. This paper describes a new neural network-based framework for learning simultaneously low-level policies as well as high-level policies, such as deciding which object to pick next or where to place it relative to other objects in the scene. A key feature of the proposed approach is that the policies are learned directly from raw videos of task demonstrations, without any manual annotation or post-processing of the data. Empirical results on object manipulation tasks with a robotic arm show that the proposed network can efficiently learn from real visual demonstrations to perform the tasks, and outperforms popular imitation learning algorithms.

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IJCAI Conference 2021 Conference Paper

Inferring Time-delayed Causal Relations in POMDPs from the Principle of Independence of Cause and Mechanism

Junchi Liang
Abdeslam Boularias

This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at regular or arbitrary times, with the objective of improving data-efficiency and interpretability of model-based reinforcement learning (RL) techniques. The proposed algorithm initially predicts observations with the Markov assumption, and incrementally introduces new hidden variables to explain and reduce the stochasticity of the observations. The hidden variables are memory units that keep track of pertinent past events. Such events are systematically identified by their information gains. A test of independence between inputs and mechanisms is performed to identify cases when there is a causal link between events and those when the information gain is due to confounding variables. The learned transition and reward models are then used in a Monte Carlo tree search for planning. Experiments on simulated and real robotic tasks, and the challenging 3D game Doom show that this method significantly improves over current RL techniques.

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IROS Conference 2020 Conference Paper

Learning Transition Models with Time-delayed Causal Relations

Junchi Liang
Abdeslam Boularias

This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at arbitrary times, with the objective of improving data-efficiency and interpretability of model- based reinforcement learning (RL) techniques. The proposed algorithm initially predicts observations with the Markov assumption, and incrementally introduces new hidden variables to explain and reduce the stochasticity of the observations. The hidden variables are memory units that keep track of pertinent past events. Such events are systematically identified by their information gains. The learned transition and reward models are then used for planning. Experiments on simulated and real robotic tasks show that this method significantly improves over current RL techniques.

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