| --- |
| tags: |
| - robotics |
| - diffusion-policy |
| - point-cloud |
| - bimanual-manipulation |
| --- |
| |
| # DP-T pointflow: point-cloud diffusion policies for DexJoCo `bimanual_assembly` |
| |
| Checkpoints of the pointflow arm of [DP-T](https://github.com/unichk/DP-T) |
| (design and every result: `docs/POINTFLOW_ARM.md` in that repo). The policy is the decoupled |
| two-tower diffusion transformer of the released baseline |
| [`unikai/DP-T`](https://huggingface.co/unikai/DP-T), with 128 unpooled object point tokens |
| (fixed CAD surface points posed at `obj_pose`) and 2 TCP tokens; nothing names a tip, a bore |
| or an axis, and no relation is computed for the policy. |
|
|
| | file | what | success, seeds 0-3 x 75 | fresh seeds 4-7 x 75 | |
| |---|---|---|---| |
| | `dpt_pointflow_gated_latest.ckpt` (119 MB) | gated cross-attention onto the point tokens, gates at zero, fine-tuned 100 epochs from the baseline's EMA weights | **55.0 %** (baseline 33.7 %, p < 0.001) | **52.0 %** (baseline 38.7 %, p = 0.0015) | |
| | `dpt_pointflow_tokens_flow_ep75.ckpt` (71 MB) | tokens in the decoder memory + pointflow auxiliary loss, trained from scratch, epoch 75 | 45.0 % | 47.7 % (baseline 38.7 %, p = 0.028) | |
|
|
| Paired closed-loop evaluation, `eval_dp.py --reference-protocol`, the default |
| `rand_obj/bimanual_assembly` eval config; per-episode records in `outputs/eval/pointflow_*`. |
|
|
| ```bash |
| hf download b12902136/DP-T-pointflow dpt_pointflow_gated_latest.ckpt --local-dir outputs/pretrained |
| python eval_dp.py --checkpoint outputs/pretrained/dpt_pointflow_gated_latest.ckpt \ |
| --eval-config $DEXJOCO_ROOT/configs/rand_obj/bimanual_assembly.yaml \ |
| --episodes 75 --seed 0 --render-first 0 --no-render --reference-protocol |
| ``` |
|
|
| Inputs at inference: proprioception (`state`, 46) and the live object poses (`obj_pose`, 14); |
| the CAD point clouds are built inside the policy from the DexJoCo object XMLs. Like the |
| baseline, these are `torch.save` workspace payloads (cfg + model + EMA weights; the optimizer |
| state is dropped), loaded by `eval_dp.py` with |
| `dexjoco_dp.policy.decoupled_pointflow_policy.DecoupledPointflowPolicy` from the repo. |
|
|
