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Found 9 papers, 1 papers with code
Differentiable Visual Computing for Inverse Problems and Machine Learning
This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.
How Can Large Language Models Help Humans in Design and Manufacturing?
The advancement of Large Language Models (LLMs), including GPT-4, provides exciting new opportunities for generative design.
Efficient automatic design of robots
Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior.
Zero-Shot Transfer of Haptics-Based Object Insertion Policies
In this paper we train a contact-exploiting manipulation policy in simulation for the contact-rich household task of loading plates into a slotted holder, which transfers without any fine-tuning to the real robot.
DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation
The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling.
DiffPD: Differentiable Projective Dynamics
Inspired by Projective Dynamics (PD), we present Differentiable Projective Dynamics (DiffPD), an efficient differentiable soft-body simulator based on PD with implicit time integration.
Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations
We validate the behavior of our algorithm with visualizations of the learned representation.
D3PG: Deep Differentiable Deterministic Policy Gradients
Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate.
