Vid2Param: Modelling of Dynamics Parameters from Video (1907.06422v3)

Abstract: Videos provide a rich source of information, but it is generally hard to extract dynamical parameters of interest. Inferring those parameters from a video stream would be beneficial for physical reasoning. Robots performing tasks in dynamic environments would benefit greatly from understanding the underlying environment motion, in order to make future predictions and to synthesize effective control policies that use this inductive bias. Online physical reasoning is therefore a fundamental requirement for robust autonomous agents. When the dynamics involves multiple modes (due to contacts or interactions between objects) and sensing must proceed directly from a rich sensory stream such as video, then traditional methods for system identification may not be well suited. We propose an approach wherein fast parameter estimation can be achieved directly from video. We integrate a physically based dynamics model with a recurrent variational autoencoder, by introducing an additional loss to enforce desired constraints. The model, which we call Vid2Param, can be trained entirely in simulation, in an end-to-end manner with domain randomization, to perform online system identification, and make probabilistic forward predictions of parameters of interest. This enables the resulting model to encode parameters such as position, velocity, restitution, air drag and other physical properties of the system. We illustrate the utility of this in physical experiments wherein a PR2 robot with a velocity constrained arm must intercept an unknown bouncing ball with partly occluded vision, by estimating the physical parameters of this ball directly from the video trace after the ball is released.

• The paper introduces Vid2Param, which integrates a recurrent variational autoencoder with physics models to infer dynamics from video in real time.
• The approach leverages domain randomization in simulation to ensure robust real-world performance and accurate parameter estimation without ground truth data.
• Experimental results, including a 77% success rate in robotic interception tasks, highlight Vid2Param’s potential for autonomous dynamic system control.

Overview of "Vid2Param: Modelling of Dynamics Parameters from Video"

The paper "Vid2Param: Modelling of Dynamics Parameters from Video" presents a novel approach to the online inference of dynamical parameters directly from video streams. The proposed model, Vid2Param, establishes an efficient method for extracting meaningful physical parameters from visual data, crucial for robust autonomous systems operating in dynamic environments.

Core Contribution

Vid2Param integrates a physically-based dynamics model with a recurrent variational autoencoder (VRAE), leveraging an additional loss function to impose desired physical constraints. The primary contribution is achieving real-time parameter estimation from video, facilitating direct interaction with dynamic environments through fast predictive modeling of physical states. This unified approach contrasts with standard methods that often handle tracking and system identification separately, potentially leading to suboptimal performance.

Methodology

The framework incorporates domain randomization to allow training solely in simulation, making it robust for real-world deployment without extensive retraining. The model, based on a VRAE architecture, captures the dynamics of a scene by mapping video input to latent representations encoding variables such as position, velocity, and restitution. This architecture supports probabilistic inference, critical for making future predictions necessary for action planning and control.

Experimental Evaluation

The authors validate their model through a series of controlled experiments. These involve both simulated and real-world video scenarios, where the task is to determine physical parameters of a bouncing ball with accurate trajectory prediction.

Key numerical results included:

• The Vid2Param model achieves comparable accuracy to traditional system identification methods, notably without access to ground truth trajectories.
• Demonstrations with the PR2 robot showed the model's efficacy in real-time scenarios, with a 77% success rate in intercepting a bouncing ball using predicted trajectories versus lower success rates with random policies.

Implications and Future Work

The implications of this research are significant for robotics and autonomous systems, particularly in applications involving dynamic object interaction under uncertainty. By enabling system identification directly from video, Vid2Param reduces reliance on high-fidelity sensors and pre-trained models. This increase in flexibility could broaden the application scope of autonomous systems in environments where sensor noise and uncertainty are prevalent.

Potential future work could explore extending Vid2Param for multi-object scenarios and incorporating it with additional sensory modalities, such as depth cameras, to further enhance prediction accuracy. Furthermore, experimenting with advanced domain randomization techniques and richer encoder-decoder architectures could improve generalization across diverse environments and tasks.

In summary, the Vid2Param model presents a sophisticated, effective approach to dynamic parameter prediction from video, promising substantial advancements in physical reasoning capabilities for autonomous systems.