Pick and Place Without Geometric Object Models (1707.05615v4)

Abstract: We propose a novel formulation of robotic pick and place as a deep reinforcement learning (RL) problem. Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation. In this formulation, actions are target reach poses for the hand and states are a history of such reaches. We show this approach can solve a challenging class of pick-place and regrasping problems where the exact geometry of the objects to be handled is unknown. The only information our method requires is: 1) the sensor perception available to the robot at test time; 2) prior knowledge of the general class of objects for which the system was trained. We evaluate our method using objects belonging to two different categories, mugs and bottles, both in simulation and on real hardware. Results show a major improvement relative to a shape primitives baseline.

• The paper presents a novel deep RL method that reformulates pick-and-place tasks without relying on explicit geometric object models.
• It introduces a descriptor-based MDP with volumetric REACH-GRASP descriptors to capture essential visual and spatial features.
• Experimental results demonstrate nearly perfect success in simulations and effective transfer to real-world robotic applications.

An Examination of "Pick and Place Without Geometric Object Models"

The paper "Pick and Place Without Geometric Object Models" by Marcus Gualtieri, Andreas ten Pas, and Robert Platt introduces a contemporary approach to robotic pick-and-place tasks using deep reinforcement learning (RL). This paper diverges from traditional methods by not relying on explicit geometric object models, making it a significant investigation in the domain of robotic manipulation.

Methodology Overview

Differentiating itself from classical techniques, which depend on precise shape and pose estimates of objects, this work formulates the pick-and-place challenge as a deep RL problem. The authors conceptualize actions as target reach poses for the manipulator and represent states using histories of these actions. Such an abstraction facilitates performing tasks on novel and unmodeled objects, given only sensory data at runtime and prior knowledge about object categories the system is trained on.

The approach leverages a partially observable Markov decision process (POMDP) framework, whereby object shape and pose are hidden states, with observations derived from sensor data like images or point clouds. The policy development aims to optimize expected rewards—indicative of successful object placement—by solving this POMDP.

Key Innovations

• Descriptor-Based MDP: To address the inefficiencies of conventional deep RL methods, which struggle to generalize over full 6-DOF pose space, the authors propose a descriptor-based MDP. They redefine actions and states in terms of volumetric descriptors that encapsulate crucial visual information at prospective reach actions' poses.
• Volumetric REACH-GRASP Descriptors: These descriptors form a critical component of both action and state representations. By capturing the volumetric appearance in proximity to grasp targets, they allow the system to dispense with prior geometric models.

Experimental Evaluation

The authors perform comprehensive simulations and physical experiments across different scenarios, including both isolated and cluttered environments. The system is trained to manipulate two object categories—mugs and bottles—demonstrating substantial improvements over baseline methods employing shape primitives.

Results in simulation reveal a nearly perfect success rate in object placement under isolated conditions, with marginally reduced performance in cluttered settings. Real-world trials on a UR5 robotic platform corroborate these findings, albeit with some challenges in grasping consistency and task completion speed. Noteworthy is the successful adaptation of policies trained in simulation to tangible robotics tasks, showcasing robust domain transferability.

Comparative Analysis

The paper contrasts the newly introduced method against previous work relying on shape primitives, segmentation, and specific grasps. The authors establish that their deep RL framework, devoid of extensive prior object information, surpasses the traditional shape approximation methods by a considerable margin, especially when dealing with complex regrasping tasks.

Implications and Future Prospects

This research marks an evolution in robotic manipulation, suggesting a trajectory that favors learning-based systems over model-dependent paradigms. The capacity to handle unfamiliar objects solely based on sensor insights has notable implications for applications where object geometries are diverse and dynamically changing.

Looking forward, further developments might involve expanding the descriptor-based models to encompass a broader array of object categories and environments. Enhanced computational models could facilitate more rapid learning and offer improved performance in dynamic, real-world scenarios.

In conclusion, Gualtieri et al.'s work contributes a novel perspective for tackling pick-and-place challenges and is poised to influence future AI developments in robotic manipulation, both in theoretical and applied contexts.