Planning with a Receding Horizon for Manipulation in Clutter using a Learned Value Function

Manipulation in clutter requires solving complex sequential decision making problems in an environment rich with physical interactions. The transfer of motion planning solutions from simulation to the real world, in open-loop, suffers from the inherent uncertainty in modelling real world physics. We propose interleaving planning and execution in real-time, in a closed-loop setting, using a Receding Horizon Planner (RHP) for pushing manipulation in clutter. In this context, we address the problem of finding a suitable value function based heuristic for efficient planning, and for estimating the cost-to-go from the horizon to the goal. We estimate such a value function first by using plans generated by an existing sampling-based planner. Then, we further optimize the value function through reinforcement learning. We evaluate our approach and compare it to state-of-the-art planning techniques for manipulation in clutter. We conduct experiments in simulation with artificially injected uncertainty on the physics parameters, as well as in real world tasks of manipulation in clutter. We show that this approach enables the robot to react to the uncertain dynamics of the real world effectively

Learning deep policies for physics-based robotic manipulation in cluttered real-world environments

This thesis presents a series of planners and learning algorithms for real-world manipulation in clutter. The focus is on interleaving real-world execution with look-ahead planning in simulation as an effective way to address the uncertainty arising from complex physics interactions and occlusions. We introduce VisualRHP, a receding horizon planner in the image space guided by a learned heuristic. VisualRHP generates, in closed-loop, prehensile and non-prehensile manipulation actions to manipulate a desired object in clutter while avoiding dropping obstacle objects off the edge of the manipulation surface. To acquire the heuristic of VisualRHP, we develop deep imitation learning and deep reinforcement learning algorithms specifically tailored for environments with complex dynamics and requiring long-term sequential decision making. The learned heuristic ensures generalization over different environment settings and transferability of manipulation skills to different desired objects in the real world. In the second part of this thesis, we integrate VisualRHP with a learnable object pose estimator to guide the search for an occluded desired object. This hybrid approach harnesses neural networks with convolution and recurrent structures to capture relevant information from the history of partial observation to guide VisualRHP future actions. We run an ablation study over the different component of VisualRHP and compare it with model-free and model-based alternatives. We run experiments in different simulation environments and real-world settings. The results show that by trading a small computation time for heuristic-guided look-ahead planning, VisualRHP delivers a more robust and efficient behaviour compared to alternative state-of-the-art approaches while still operating in near real-time

Learning Physics-Based Manipulation in Clutter: Combining Image-Based Generalization and Look-Ahead Planning

Physics-based manipulation in clutter involves complex interaction between multiple objects. In this paper, we consider the problem of learning, from interaction in a physics simulator, manipulation skills to solve this multi-step sequential decision making problem in the real world. Our approach has two key properties: (i) the ability to generalize and transfer manipulation skills (over the type, shape, and number of objects in the scene) using an abstract image-based representation that enables a neural network to learn useful features; and (ii) the ability to perform look-ahead planning in the image space using a physics simulator, which is essential for such multi-step problems. We show, in sets of simulated and real-world experiments (video available on https://youtu.be/EmkUQfyvwkY), that by learning to evaluate actions in an abstract image-based representation of the real world, the robot can generalize and adapt to the object shapes in challenging real-world environments

Learning how to combine sensory-motor functions into a robust behavior

AbstractThis article describes a system, called Robel, for defining a robot controller that learns from experience very robust ways of performing a high-level task such as “navigate to”. The designer specifies a collection of skills, represented as hierarchical tasks networks, whose primitives are sensory-motor functions. The skills provide different ways of combining these sensory-motor functions to achieve the desired task. The specified skills are assumed to be complementary and to cover different situations. The relationship between control states, defined through a set of task-dependent features, and the appropriate skills for pursuing the task is learned as a finite observable Markov decision process (MDP). This MDP provides a general policy for the task; it is independent of the environment and characterizes the abilities of the robot for the task