Human-like Planning for Reaching in Cluttered Environments

Abstract

Humans, in comparison to robots, are remarkably adept at reaching for objects in cluttered environments. The best existing robot planners are based on random sampling in configuration space-which becomes excessively highdimensional with a large number of objects. Consequently, most of these planners suffer from limited object manipulation. We address this problem by learning high-level manipulation planning skills from humans and transfer these skills to robot planners. We used virtual reality to generate data from human participants whilst they reached for objects on a cluttered table top. From this, we devised a qualitative representation of the task space to abstract human decisions, irrespective of the number of objects in the way. Based on this representation, human demonstrations were segmented and used to train decision classifiers. Using these classifiers, our planner produced a list of waypoints in task space. These waypoints provide a high-level plan, which can be transferred to an arbitrary robot model and used to initialize a local trajectory optimiser. We evaluated this approach through testing on unseen human VR data, a physics-based robot simulation and real robot experiments. We find that this human-like planner outperforms a state-of-the-art standard trajectory optimisation algorithm and is able to generate effective strategies for rapid planning, irrespective of the number of objects in a cluttered environment. Our dataset and source code are publicly available 1 .

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

“…Sakamoto et al [26] proposed a picking system that first uses robot pushing [27,28,29], to move one cuboid to the other and thereafter grasps both cuboids in separate actions. In this paper, we take a single-step push-grasp action and derive conditions for multi-object grasping under the frictional point contact model.…”

A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System

“…Traditional methods include things like programming, convex optimization, and heuristic approaches, to name a few. The advantages and disadvantages of these methods can be better understood by contrasting them with optimisation algorithms on a qualitative level [16,17]. The mathematical approach known as Lyapunov optimization is an example of a classical mathematical approach [18].…”

Robust Physics-Based Manipulation by Interleaving Open and Closed-Loop Execution

“…The proof is straightforward by applying steps in Eq. [4][5][6][7][8] to the real-world system in Eq. 9.…”

“…One traditional approach for such tasks is to generate a manipulation plan and execute the actions open-loop, without feedback [4,5,6]. Such plans fail in the face of uncertaintyin state estimation and physics predictions [7].…”