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Robot Utility Models (RUM)
New York University; Hello Robot Inc.; Meta · 2024-09
VAVANot discloseddiscrete tokens
Uses a $25-BoM iPhone collection tool to gather about 1,000 demos per task across about 40 environments, deploying zero-shot in new environments.
Paper
Robot Utility Models: General Policies for Zero-Shot Deployment in New EnvironmentsarXiv 2409.05865
Overview
- Category
- VA
- Type
- VA
- Released
- 2024-09
- Organization
- New York University; Hello Robot Inc.; Meta
- Open weights
- Yes
- Parameters
- Not disclosed
Architecture
- Backbone LLM
- None
- Vision encoder
- ResNet34 (HPR init)
- Action head
- VQ action codes (discrete tokens)
- Chunk
- Not disclosed
- Control rate
- Not disclosed
- Architecture (original summary)
- HPR-initialized ResNet34 vision encoder + VQ-BeT policy (chosen over DP for performance·robot CPU latency); iPhone camera input
Training
- Pretraining accelerators
- N/A
- Pretraining duration
- N/A
- Pretraining steps & batch
- N/A
- GPU/TPU-hours
- Not disclosed
- Fine-tuning
- Per-task models: 2 A100, 24–48 hours (phrased as 'all of our models', so unclear whether per model), 500 epochs
- Data type
- Human video (handheld iPhone Stick-v2 demonstrations)
- Data scale
- About 1,000 demos per task, about 40 environments (about 25 per environment on average); door opening 1,200, drawer 525
- Main datasets
- Stick-v2 in-house collection
- Tasks & evaluation
- Opening cabinet doors, opening drawers, picking up tissues, picking up paper bags, righting fallen objects; zero-shot deployment in unseen environments
- Robot
- Hello Robot Stretch (also tested on xArm 7)
Inference
- Inference device & speed
- Stretch onboard CPU, iPhone camera; latency not disclosed
- Inference memory
- Not disclosed
- Weights bf16 (calc.)
- Not disclosed
- Where it runs
- Onboard (Stretch built-in CPU)
Reinforcement learning & base
- Reinforcement learning
- Not used · Imitation learning. At deployment, a gpt-4o verifier detects failures and retries
- Base control
- Fixed · Navigation assumed to be a separate component; policy outputs only EE-relative 6D actions at the task location
Notes
- Key features
- Uses a $25-BoM iPhone collection tool to gather about 1,000 demos per task across about 40 environments, deploying zero-shot in new environments. An external GPT-4o verifier detects failures and triggers retries.
- Notes
- The verifier gpt-4o-2024-05-13 is a component external to the policy; the policy itself takes no language input. Paper states release of code·data·models·hardware design.
- Verification
- Confirmed
Other models in this category
- Mobile ALOHA2024-01
- ALOHA Unleashed2024-10
- HPT2024-09
- BAKU2024-06
- VQ-BeT2024-03
- ScaleDP2024-09†
- UMI2024-02
- DP32024-03
- Data Scaling Laws2024-10
- RISE2024-04
- EgoMimic2024-10
- InterACT2024-09†
- CARP2024-12†
- Dense Policy2025-03
- Chain-of-Action2025-06
- BRS (WB-VIMA)2025-03