Learning to Predict Contact Force Distributions from Vision Leveraging Object Geometry Priors

Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robots with a similar ability. To collect paired data of vision and forces, we use a rigid-body simulator commonly adopted in robotics. However, unlike simulators that output noisy point forces, humans are able to make consistent predictions even in unfamiliar situations. Based on this observation, we hypothesize that predicting smooth force distributions rather than raw point forces can improve both force prediction itself and downstream task performance. To validate this hypothesis, we construct a model that predicts three-dimensional force distributions from a single RGB image of piled daily objects. The target distribution is generated by applying statistical smoothing to point forces obtained from the simulator. Moreover, by incorporating object geometry into the smoothing process, we aim to account for variations in contact states and achieve more consistent vision-based predictions. We conduct extensive evaluations in both simulation and real environments. Results show that our approach improves prediction accuracy, enhances downstream task performance through smoothing, and further benefits from geometry-guided smoothing. Remarkably, the trained model generalizes effectively to real-world scenes despite being trained solely in simulation.

Visual Imitation Learning of Non-Prehensile Manipulation Tasks with Dynamics-Supervised Models

Unlike quasi-static robotic manipulation tasks like pick-and-place, dynamic tasks such as non-prehensile manipulation pose greater challenges, especially for vision-based control. Successful control requires the extraction of features relevant to the target task. In visual imitation learning settings, these features can be learnt by backpropagating the policy loss through the vision backbone. Yet, this approach tends to learn task-specific features with limited generalizability. Alternatively, learning world models can realize more generalizable vision backbones. Utilizing the learnt features, task-specific policies are subsequently trained. Commonly, these models are trained solely to predict the next RGB state from the current state and action taken. But only-RGB prediction might not fully-capture the task-relevant dynamics. In this work, we hypothesize that direct supervision of target dynamic states (Dynamics Mapping) can learn better dynamics-informed world models. Beside the next RGB reconstruction, the world model is also trained to directly predict position, velocity, and acceleration of environment rigid bodies. To verify our hypothesis, we designed a non-prehensile 2D environment tailored to two tasks: "Balance-Reaching" and "Bin-Dropping". When trained on the first task, dynamics mapping enhanced the task performance under different training configurations (Decoupled, Joint, End-to-End) and policy architectures (Feedforward, Recurrent). Notably, its most significant impact was for world model pretraining boosting the success rate from 21% to 85%. Although frozen dynamics-informed world models could generalize well to a task with in-domain dynamics, but poorly to a one with out-of-domain dynamics.