Source-linked AI summary
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Ankit Goyal, Arsalan Mousavian, Chris Paxton, Yu-Wei Chao, Brian Okorn, Jia Deng, Dieter Fox
TL;DR
Robotic rearrangement from images is challenging when objects are unknown and planning depends on accurate models and poses. IFOR estimates transformations with optical flow and iteratively rearranges objects; the authors report real-world robot experiments and performance gains over previous work.
Problem
Image-guided rearrangement must handle unknown objects, while conventional planning often assumes known objects, detailed models, and accurate poses.
Source: §1 · pp. 1–2Method
IFOR uses optical flow, depth, and segmentation to estimate object transformations, then iteratively plans and executes rearrangement actions.
Source: §1 · p. 2, §4.1 · p. 6Results
Takeaways & Limitations
IFOR transfers from synthetic training data to real-world rearrangement of novel objects in cluttered scenes.
Source: §1 · p. 2Evaluation covers planar rotations only because the models were trained only on data with planar rotations.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow Minimization for Robotic Object Rearrangement, an end-to-end method for the challenging problem of object rearrangement for unknown objects given an RGBD image of the original and final scenes. First, we learn an optical flow model based on RAFT to estimate the relative transformation of the objects purely from synthetic data. This flow is then used in an iterative minimization algorithm to achieve accurate positioning of previously unseen objects. Crucially, we show that our method applies to cluttered scenes, and in the real world, while training only on synthetic data. Videos are available at https://imankgoyal.github.io/ifor.html.
1. Introduction
The paper targets image-guided rearrangement of unknown objects, where conventional planning often depends on known models and accurate poses. IFOR uses optical flow to estimate object transformations and supports translation and planar rotation for unseen objects.
1. Introduction
- Robotic rearrangement matters for everyday activities such as setting a table, putting away groceries, and organizing a desk.The passage frames this capability as useful for robots assisting people with everyday tasks.Source: §1 · p. 1
- An RGB-D goal image provides a way to specify a desired scene that can be captured once and used to restore objects from other configurations.The paper illustrates this with a user photographing a preferred table setting.Source: §1 · p. 1
- Prior task-and-motion-planning approaches often assume known objects and high-fidelity 3D models, while accurate pose estimates can make the pipeline susceptible to vision errors.These assumptions limit use in unseen environments or environments without models.Source: §1 · pp. 1–2
- IFOR handles unknown objects with translation and planar rotation from RGB-D input, extending image-guided rearrangement beyond translation-only approaches.The authors describe this as the first approach, to their knowledge, with these capabilities.Source: §1 · p. 2
- IFOR reformulates rearrangement as iterative optical-flow minimization, using depth and segmentation to derive dense 3D correspondences and optimize object transformations.The authors report transfer to the real world after training entirely on synthetic data.Source: §1 · p. 2
2. Related Work
Related work includes model-based planning and learned rearrangement methods, many of which handle image-specified goals but restrict object motion to planar translation. IFOR uses optical flow to estimate transformations and connects rearrangement to real-world robotic evaluation.
Related Work
- Conventional task-and-motion planning [10] [15] [16] [26] can require task-specific knowledge and accurate 3D environment models, limiting generalization.Related learning-based work addresses components such as grasp planning, motion planning, and spatial reasoning.Source: §2 · p. 2
- Prior image-goal rearrangement methods [51] consider planar translation without orientation changes, whereas IFOR uses optical flow to infer fuller transformations.The paper relates this contrast to generic feature descriptors that are not rotation-sensitive.Source: §2 · p. 2
- IFOR uses optical flow between current and goal images to establish desired object transformations, including for large displacements from arbitrary goal images.The overview depicts these estimates feeding a robot planning and execution pipeline for unknown geometry.Source: §2 · pp. 2–3, Figure 2 · p. 3
- Unlike most prior embodied-AI work evaluated only in simulation, IFOR also evaluates its method on a real-world robotic platform.The paper situates rearrangement as a canonical embodied-AI task [2].Source: §2 · p. 3
3. Method
IFOR repeatedly estimates object transformations from current and goal RGB-D scenes, then plans and executes pick-and-place actions. Its perception pipeline combines learned flow, unseen-object segmentation, and robust pose estimation; its planner accounts for collision and kinematic feasibility.
3. Method
- At each iteration, IFOR processes current and goal RGB-D scenes, estimates object transformations, selects a feasible object movement, executes it, and observes the scene again.The system repeats this perception-and-planning cycle until estimated changes fall below rotation and translation thresholds.Source: §3 · p. 3, §3.2 · p. 5
3.1. Perception
- RAFT flow estimation is adapted to rearrangement, where objects may undergo large translations and rotations rather than the small displacements typical of consecutive video frames.The authors retrain RAFT on synthetic cluttered-scene pairs with large randomly sampled transformations; the dataset contains about 54K training and 1,000 test samples.Source: §3.1 · pp. 3–4, Figure 3 · p. 4
- Pre-trained UCN segments unseen objects by clustering per-pixel embeddings, supplying the object grouping that optical flow alone lacks.Pixels from the same instance have similar embeddings, while embeddings differ across instances.Source: §3.1 · p. 4
- Depth, camera intrinsics, and predicted flow yield 3D correspondences, which are used to fit a rigid transform for each object.RANSAC handles flow-correspondence outliers, which are especially common under extreme transformations with little shared visible surface.Source: §3.1 · p. 4
3.2. Planning and Execution
- The planner ranks collision-free predicted moves by relative rotation and translation, then greedily chooses objects with larger transformations.If no direct move is feasible, it tries a random collision-free placement; given correct estimated transforms, the policy is described as guaranteed to converge.Source: §3.2 · p. 5
4. Experiments
In real-world comparisons, users preferred IFOR over NeRP for both position and full-pose rearrangement. Simulation ablations found that rearrangement-trained flow, RANSAC-based transform estimation, and planar-rotation assumptions improve accuracy, while heuristic planning approaches a no-planning upper bound.
4.1. Real World Experiments
- IFOR more closely matches goal images and orientations than NeRP in both qualitative examples.The comparison uses similar target images and the same pick-and-place components, differing in final-pose estimation.Source: Figure 5 · p. 6, §4.1 · p. 7
- Users chose IFOR over NeRP in 94% of position-only comparisons and 92% of full-pose comparisons.The full-pose rating includes both position and orientation.Source: Figure 6 · p. 6, §4.1 · p. 7
4.2. Ablation Studies
- RANSAC-based optimization with rearrangement-trained RAFT achieves significantly lower transform errors than the learning-based baseline.Table 1 compares one-step pose transformation estimates on simulated data.Source: Table 1 · p. 6, §4.2 · p. 7
- Training RAFT on rearrangement scenes with large translation and orientation changes is crucial for accurate relative-transform prediction.Pre-trained RAFT was trained on video sequences.Source: §4.2 · p. 7
- Placement accuracy drops with imperfections in the learned collision checker, and the method benefits from better collision detection.The checker determines whether a predicted translation and rotation is accepted.Source: §4.2 · pp. 7–8, Table 2 · p. 8
- Heuristic planning performs close to the teleportation policy’s no-planning upper bound, while teleporting all objects at once is impractical for real robots.The teleportation policy removes planning constraints and achieves significantly better results than the planned policy.Source: §4.2 · p. 7, Table 2 · p. 8
- Adding a planar-rotation assumption significantly improves accuracy over optimizing unrestricted relative SE(3) transforms.The planar assumption keeps only rotation around the z-axis.Source: §4.2 · p. 8
- IFOR’s accuracy generally improves over more steps, but bottles and dishware remain difficult because geometry, texture, symmetry, and image resolution complicate matching.Small bottles may lack sufficient resolution for texture matching; dishware often has little or no texture.Source: §4.2 · p. 8, Figure 8 · p. 8
5. Discussion
IFOR is presented as a real-world system for rearranging unseen objects, with reported gains over previous work. The discussion identifies planar-rotation evaluation and execution monitoring as future directions.
5. Discussion
- IFOR handles image-based rearrangement of unseen objects with translation and rotation, and experiments report a significant performance boost over previous work.Source: §5 · p. 8
- Evaluation covered planar rotations because the models were trained only on planar-rotation data.Source: §5 · p. 8
- Monitoring execution to account for in-gripper object motion and update placement poses remains a future direction.Source: §5 · p. 8