What Is Im2Flow2Act?

Im2Flow2Act is named in Flow as the Cross-Domain Manipulation Interface (arXiv 2407.15208) by Mengda Xu, Zhenjia Xu, Yinghao Xu, Cheng Chi, Gordon Wetzstein, Manuela Veloso and Shuran Song, from Stanford University, Columbia University, J.P. Morgan AI Research and Carnegie Mellon University, first submitted on 21 July 2024 and last revised on 4 October 2024. The official repository labels it a CoRL 2024 paper. The abstract frames the problem as bridging two gaps: between embodiments, human and robot, and between environments, real world and simulation. The authors propose object flow as the manipulation interface that spans both.

Firsthand is not affiliated with the Im2Flow2Act authors and makes no claim that any model, Im2Flow2Act included, was trained on Firsthand data. Firsthand captures human egocentric video, custom collection, fast and on demand, to a written spec, with depth derived from a calibrated stereo rig, 3D hand pose at 21 joints per hand and 6-DoF IMUs at 200 Hz, delivered in RLDS, LeRobot format or WebDataset under a buyer-owned commercial license, with one task instruction per episode. Point tracks, optical flow, object flow, 2D or 3D trajectory labels, keypoint labels, robot trajectories, teleoperation data, synthetic data and language annotations beyond that instruction are not Firsthand deliverables.

Per the paper, object flow is the exclusive motion of the manipulated object, excluding any background or embodiment. The authors list four benefits: it can describe rigid, articulated and deformable objects; it is independent of the agent’s embodiment because it describes the change an action causes rather than the action; it focuses on motion rather than appearance, which they say reduces the sim-to-real gap; and it is interpretable, so a person can read and select a flow.

The flow generation network is trained on human demonstration videos. Given the initial scene image and a task description, it generates the complete object flow for the task once, at the start. The authors build it on the AnimateDiff video generation model, detect the object with Grounding DINO and track keypoints with TAPIR.

The flow-conditioned policy is trained on simulated robot play data. It takes the generated flow, the current tracked keypoints and the robot’s proprioception, and outputs a 6-DoF end-effector position and a 1-DoF gripper state through a diffusion action head. A temporal alignment module estimates the remaining flow from task progress.

Per the paper, no real-world robot data is collected. The robot side uses simulated exploration: the authors report 4,800 random exploration trajectories from a UR5e robot in MuJoCo, across rigid, articulated and deformable objects. The human side is real-world hand demonstration video for four tasks, recorded with a RealSense camera at 30 FPS. These are the authors’ figures, and Firsthand has not verified them.

The authors evaluate four real-world tasks: pick-and-place, pouring, opening a drawer and folding cloth. They report an average success rate of 81% across the four, and, in their Table 2 for language-conditioned execution, 90%, 80%, 85% and 70% respectively. They also report that the method outperforms ATM by an average of 30% across four simulated tasks. These are the authors’ figures, measured on their setup; Firsthand has not verified or reproduced them.

Per the paper, 2D flow is ambiguous for 3D actions, with pouring failures tied to imprecise movement along the camera depth axis, and it struggles with out-of-plane rotation such as screwing. Simulating deformable objects remains hard, which the authors link to a drop in cloth folding. The system assumes a camera viewpoint calibrated between simulation and testing, flow as an abstraction limits dexterous tasks such as in-hand manipulation, and it relies on accurate object detection.

The first table sets Im2Flow2Act beside three site entries. The Im2Flow2Act paper discusses both Track2Act and ATM, and its comparisons are the authors’ own. It does not discuss Firsthand trajectory data, which is a measured wrist and hand path, a different object from generated image-space flow.

Per the paper, the flow stage needs human demonstration video plus a task description, and an initial depth image gives 3D keypoints at the start. The paper does not state whether egocentric capture would work in the video role, so that is Not stated. It also records the demonstrations with a fixed RealSense camera and assumes a calibrated viewpoint.

Firsthand states real human egocentric capture with one task instruction per episode and depth derived from a calibrated stereo rig. That is a note about format, not a claim of compatibility, and any object flow would come from tools the buyer runs, not from Firsthand.

When a program needs real human demonstrations that public or synthetic data does not cover, specified field by field. Firsthand does not build or train models. Confirm the fields on a Spec Pilot, a small fast first batch; the median from signed spec to first validated batch is 48 hours where coverage is deep, three to four weeks where thin.

What is Im2Flow2Act?

A published framework (arXiv 2407.15208) that uses object flow as the interface between human demonstration video and a robot policy trained in simulation, with no real-world robot training data. It is third-party research.

What is object flow in Im2Flow2Act?

Per the paper, the motion of the manipulated object alone, excluding any background or embodiment, which makes it independent of whether a human or a robot acts.

What results do the authors report?

An average success rate of 81% across four real-world tasks, per the authors. These are the authors’ figures; Firsthand has not verified them.

How does Im2Flow2Act differ from ATM?

Per the authors, ATM predicts grid flow that also captures embodiment motion and needs in-domain robot data, while Im2Flow2Act generates only object flow and uses simulated robot data.

Does Firsthand use Im2Flow2Act, and can I request a custom dataset?

Firsthand is not affiliated with the Im2Flow2Act authors and claims no use, training or compatibility. Point tracks, optical flow, object flow, trajectory labels, robot trajectories and teleoperation data are not Firsthand deliverables. You can request a custom dataset.