TossingBot: Learning to Throw Arbitrary Objects with Residual Physics (1903.11239v3)

Abstract: We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. Videos are available at https://tossingbot.cs.princeton.edu

• The paper presents a novel hybrid system that integrates physics-based predictions with learned residuals to improve robotic grasping and throwing.
• It employs a deep neural network alongside a physics controller to adjust release parameters, achieving 85% throwing accuracy through self-supervised learning.
• Experimental results in simulation and real-world tests show significant efficiency gains, with the robot performing over 500 picks per hour in dynamic environments.

Analysis of "TossingBot: Learning to Throw Arbitrary Objects with Residual Physics"

In "TossingBot: Learning to Throw Arbitrary Objects with Residual Physics," the authors present a system that enhances robotic manipulation through dynamic capabilities, enabling a robot to grasp and throw objects into targets beyond its immediate reach. The paper introduces an innovative approach that integrates physics-based modeling and deep learning within a unified framework, termed "Residual Physics." This approach allows a robot to exploit dynamic dexterity, significantly augmenting its operational range and task efficiency.

Overview

TossingBot is structured around learning control parameters for grasping and throwing by processing visual observations from an RGB-D camera. The system leverages trial-and-error learning to optimize these primitives, thus adapting its grasping mechanisms to facilitate more accurate throws. Notably, TossingBot achieves over 500 mean picks per hour, combining high-speed grasping with 85% throwing accuracy.

The methodological core of this research lies in the hybridization of analytical and data-driven models. The system incorporates a physics-based controller to estimate motion essentials like release velocities, while learned residuals adjust these preliminary predictions. This allows TossingBot to account for real-world dynamic variations such as aerodynamics and multi-object interactions that are difficult to model explicitly.

Key Components

1. Joint Learning Framework: TossingBot employs a deep neural network to simultaneously predict parameters for both grasping and throwing. It integrates spatial feature representations with physics-based estimates, directing its predictions to optimize for successful object reaches and throws.
2. Residual Physics: This is a distinctive aspect where TossingBot superimposes learned corrective factors (residuals) on physics-derived predictions. This strategy mitigates inaccuracies stemming from model simplifications or environmental uncertainties.
3. Self-Supervised Learning: The system utilizes a self-supervised learning paradigm, continuously refining its grasping and throwing strategies based on observed outcomes of prior actions. This process enables TossingBot to learn autonomously and adapt to new object types and placements.

Results and Discussion

The experimental validation is twofold: simulation and real-world applications. The results show a stark improvement in performance when employing Residual Physics over purely regression-based methods or physics-only strategies. For instance, in simulations using complex objects like hammers, TossingBot with residual learning demonstrated superior throwing success rates, indicating its ability to adapt throwing mechanics adaptive to object-specific properties.

Simultaneously, the approach fosters generalization to unseen objects and target locations, maintaining high throwing precision. This adaptability is rooted in leveraging physics-based calculations as a reliable scaffold for further machine learning enhancements.

On a practical front, the research highlights significant progress toward operational deployments in logistics and warehouse environments. By enhancing pick-and-place tasks through throwing, the system substantially improves spatial efficiency and time economy.

Implications and Future Directions

While TossingBot addresses several pressing challenges in robotic manipulation, its limitations — such as dependency on assumed object rigidity and visual data — pinpoint areas ripe for future exploration. Introducing tactile or haptic sensing could further refine grasp planning under variability. Additionally, extending the framework to adaptive control systems that predict interactions with non-rigid objects could expand the applicability of TossingBot.

Furthermore, the concept of leveraging residual models holds potential beyond throwing — applicable to a spectrum of tasks requiring real-time adaptation to dynamic contexts. This path could lead to advancements in areas like autonomous navigation or dexterous manipulation in complex terrains.

In conclusion, this paper's contributions lay robust groundwork for enhancing dynamic robotic capabilities and effectively integrating physics-driven insights into learning-based systems. Through TossingBot, the research advances our understanding of how complementary analytical and empirical strategies can unlock novel robotic behaviors, a prospect that augurs promising advancements in AI-driven automation.