Differentiable SpaTiaL is the first fully tensorized, end-to-end differentiable symbolic spatio-temporal logic framework that enables gradient-based trajectory optimization and parameter learning for robotic manipulation under geometric and temporal constraints.
End- to-end and highly-efficient differentiable simulation for robotics
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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Tempered sequential Monte Carlo samples from a Boltzmann-tilted distribution over controllers to optimize trajectories and policies under differentiable dynamics.
A first-order Sobolev loss for diffusion policies enables warm-starting trajectory optimization solvers with 2×–20× speedup and fewer diffusion steps, using very few training trajectories.
EUPHORIA is a hybrid framework using meta-learning via graph hypernetworks, physics-biased attention in graph transformers, and residual stability correction for few-shot adaptable robotic assembly planning.
citing papers explorer
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Differentiable SpaTiaL: Symbolic Learning and Reasoning with Geometric Temporal Logic for Manipulation Tasks
Differentiable SpaTiaL is the first fully tensorized, end-to-end differentiable symbolic spatio-temporal logic framework that enables gradient-based trajectory optimization and parameter learning for robotic manipulation under geometric and temporal constraints.
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Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics
Tempered sequential Monte Carlo samples from a Boltzmann-tilted distribution over controllers to optimize trajectories and policies under differentiable dynamics.
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Accelerating trajectory optimization with Sobolev-trained diffusion policies
A first-order Sobolev loss for diffusion policies enables warm-starting trajectory optimization solvers with 2×–20× speedup and fewer diffusion steps, using very few training trajectories.
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EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly
EUPHORIA is a hybrid framework using meta-learning via graph hypernetworks, physics-biased attention in graph transformers, and residual stability correction for few-shot adaptable robotic assembly planning.