Comments (1)
Hongkai's corridor? perhaps even with single-integrator dynamics
I like the idea of using single (or maybe double) integrator dynamics in this case. It certainly makes the trajectory optimization a lot easier. One challenge I observe when working with the cart-pole dynamics is that because the dynamics is highly nonlinear, the shooting method doesn't work well when I add the score matching cost. But I expect the trajectory optimization to work much easier for the linear system.
As you mentioned in #40, we have several "extrapolative regime" (car in the corridor, box pushing, etc), I have been thinking hard on the "interpolative regime". One idea is that there are some stepping stones for the half cheetah, and we have collected data for half cheetah walking on these stepping stones, but in-between the stepping stones, the ground has completely different material (like sand or mud), so if we interpolate between the stepping stones where we collect the data, this interpolated dynamics is bad. Therefore we want to use the score function to force the half-cheetah to step on the stepping stones. WDYT?
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Related Issues (19)
- Design Pattern Notes: Dynamics and Policy Class Coverage
- ScoreFunctionEstimator.train_network has incorrect sigma_min by default HOT 1
- ScoreFunction should normalize and denormalize the input HOT 2
- Generalize to Multiple Dimensions
- Architecture Study for Noise Conditioning HOT 2
- Baselines & Selling Points HOT 1
- Experiments to try and include
- Adding U-Net for diffusion model HOT 6
- Add Noise-Conditioned Score Estimator
- Question about dynamics training
- Thoughts on choosing experiments HOT 5
- D4RL Mujoco Experiments
- Diffusion Planning Comparison HOT 2
- Use torch.Dataset instead of our own Dataset?
- Compare against Janner's approach
- Add a keypoint based pushing example
- Add a cart-pole example with neural network dynamics
- Meeting w/ Abhishek
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