Role of Uncertainty in Anticipatory Trajectory Prediction for a Ping-Pong Playing Robot (2312.03024v1)

Abstract: Robotic interaction in fast-paced environments presents a substantial challenge, particularly in tasks requiring the prediction of dynamic, non-stationary objects for timely and accurate responses. An example of such a task is ping-pong, where the physical limitations of a robot may prevent it from reaching its goal in the time it takes the ball to cross the table. The scene of a ping-pong match contains rich visual information of a player's movement that can allow future game state prediction, with varying degrees of uncertainty. To this aim, we present a visual modeling, prediction, and control system to inform a ping-pong playing robot utilizing visual model uncertainty to allow earlier motion of the robot throughout the game. We present demonstrations and metrics in simulation to show the benefit of incorporating model uncertainty, the limitations of current standard model uncertainty estimators, and the need for more verifiable model uncertainty estimation. Our code is publicly available.

• The paper demonstrates the integration of uncertainty estimation in pre-hit trajectory predictions to inform early robot control decisions.
• It employs a data-driven framework utilizing visual cues like player pose and racket orientation to forecast ball movement in fast-paced table tennis.
• Simulation results reveal modest performance improvements, highlighting the need for more robust uncertainty estimators in dynamic robotic environments.

Introduction

Robotic systems are increasingly being applied in dynamic and fast-paced environments, and one intriguing domain presenting substantial challenges is that of table tennis. Achieving competent play in such a setting involves not only reacting to a moving ball but also anticipating future game states based on the opponent's movements. This research focuses on a visual modeling, prediction, and control system for a ping-pong playing robot that leverages uncertainty estimation to inform earlier robot motions during the game.

Anticipatory Prediction Framework

The framework adopted in this paper uses data-driven approaches to forecast the ping-pong ball’s post-hit trajectory based on a series of pre-hit visual cues such as the player's 3D pose and racket orientation. The model aims to generate early predictions before the opponent's hit, allowing the robot to strategize and adapt its movement to improve its playing performance. However, the complex dynamics of ping-pong, including spin and air resistance considerations, present significant challenges for accurate predictions. The research highlights this by experimentally demonstrating the limitations of current uncertainty estimators in effectively guiding the controller's movement.

Model Uncertainty Estimation

Quantifying the uncertainty of neural network predictions is crucial for implementing anticipatory strategies in robotic control. If predictions about the ping-pong ball's trajectory come with a high level of confidence, robots can aggressively position themselves to strike the ball. If the confidence level is low, a more conservative strategy may be warranted. The paper explores several methods of uncertainty estimation, including ensembles of models and conformal prediction techniques, to enable the robot controller to adjust its speed proportionally to the model's confidence. However, results indicated that commonly used uncertainty estimators do not reliably correlate with actual model performance, leading to either over-restriction or insufficiently informed robotic movements.

Simulation Results and Future Work

In simulations, the system demonstrated that integrating pre-hit anticipatory models into the robot’s control logic can enhance its ping-pong playing capabilities, including better directional guidance and increased chances of successfully returning the ball. Notably, the performance improvements were modest due to the simplistic nature of the experimented uncertainty estimators, underscoring an opportunity for more robust techniques in the future. The challenges and findings from this research suggest that ping-pong remains an effective testbed for improving model uncertainty estimation methodologies and continuing advancements in robotic interaction in complex and fast-paced scenarios.