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Visual Imitation Made Easy
Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, Lerrel Pinto
TL;DR
Visual imitation learning needs interfaces that support large-scale, diverse demonstrations beyond restrictive teleoperation and kinesthetic teaching. The paper introduces DemoAT, using reacher-grabber tools, visual action extraction, and behavior cloning, and reports 87.5% pushing and 62.5% stacking success on previously unseen objects.
Problem
Existing kinesthetic and teleoperation interfaces make large-scale collection difficult and constrain demonstrations to restrictive laboratory settings with limited object and environment diversity.
Method
DemoAT uses a reacher-grabber as both a visual demonstration tool and robot end-effector, extracts trajectories with Structure from Motion and finger states with a detector, then trains behavior-cloning policies.
Results
87.5% success on pushing and 62.5% on stacking are reported for previously unseen objects, following evaluation with diverse demonstrations and data augmentation.
Takeaways & Limitations
The results show that assistive-tool data collection combined with behavior cloning and data augmentation can support out-of-distribution robotic manipulation performance.
Takeaways & Limitations
Action-label extraction can be noisy in lightly textured, clean, and high-dynamic-range scenes, leading the authors to discard approximately 6% of aberrant demonstrations.
Abstract
from arXiv · showhide
Visual imitation learning provides a framework for learning complex manipulation behaviors by leveraging human demonstrations. However, current interfaces for imitation such as kinesthetic teaching or teleoperation prohibitively restrict our ability to efficiently collect large-scale data in the wild. Obtaining such diverse demonstration data is paramount for the generalization of learned skills to novel scenarios. In this work, we present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. To extract action information from these visual demonstrations, we use off-the-shelf Structure from Motion (SfM) techniques in addition to training a finger detection network. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task. For both tasks, we use standard behavior cloning to learn executable policies from the previously collected offline demonstrations. To improve learning performance, we employ a variety of data augmentations and provide an extensive analysis of its effects. Finally, we demonstrate the utility of our interface by evaluating on real robotic scenarios with previously unseen objects and achieve a 87% success rate on pushing and a 62% success rate on stacking. Robot videos are available at https://dhiraj100892.github.io/Visual-Imitation-Made-Easy.
1 Introduction
The paper addresses restrictive imitation interfaces by introducing DemoAT, which uses reacher-grabber tools to collect diverse visual demonstrations and transfer learned skills to a matching robot end-effector. Across pushing and stacking, the framework combines extracted action labels, behavior cloning, and data augmentation to improve performance on unseen objects.
- Motivation: Kinesthetic teaching and teleoperation make large-scale collection difficult and constrain demonstrations to restrictive laboratory settings.Such datasets often contain little object or environmental variability, limiting generalization to previously unseen situations.
- Proposed interface: DemoAT uses a commercially available reacher-grabber as both a demonstration device and the robot’s end-effector.This design targets intuitive, inexpensive collection while reducing the transfer gap between demonstrations and robot execution.
- Pipeline: The framework extracts tool trajectories with Structure from Motion and gripper configurations with a trained finger detector before applying standard behavior cloning.The robot uses the same tool setup as the demonstration end-effector.
- Evaluation: 1000 demonstrations per task support evaluation on non-prehensile pushing and prehensile stacking across multiple environments and objects.The collected diversity is intended to support generalization to novel objects.
- Results: 87.5% success on pushing and 62.5% on stacking are reported for previously unseen objects after data augmentation.Naive behavioral cloning achieved 62.5% in pushing and 29.2% in stacking before augmentation.
2 Related Work
Related work frames imitation interfaces and behavior cloning around the challenge of obtaining suitable visual demonstrations for robotic manipulation. It also situates data augmentation as a standard strategy for improving learning from limited data.
- Scope: The paper is positioned within broader imitation-learning research while focusing specifically on interfaces, behavior cloning, and augmentation for visual robotic manipulation.The related-work discussion directs readers to a comprehensive imitation-learning review.
- Interfaces for Imitation: Teleoperation supports diverse robotic tasks but is difficult to design for robotic manipulation, while kinesthetic demonstrations can introduce undesirable human-arm artifacts for visuomotor policies.These limitations motivate alternatives that provide visual observations without restrictive interfaces.
- Behavior Cloning in Imitation: Behavior cloning learns an observation-to-action mapping through supervised learning and has been applied to games, self-driving, and drone navigation.The paper presents it as the simplest form of imitation learning.
- Data Augmentation in Learning: Data augmentation is widely used to inject additional knowledge and reduce overfitting in machine learning.Prior computer-vision systems used transformations such as random flips and crops to improve classification accuracy.
3 Method
The method combines a camera-equipped assistive reacher-grabber, visual action extraction, and supervised behavior cloning to train policies transferable to a robot with the same end-effector. It collects diverse trajectories for pushing and stacking while addressing action-label noise through inspection and a finger detector.
- 3.1 The DAT imitation framework: DemoAT uses a plastic 19-inch assistive tool with an RGB camera and a 3D-printed mount to collect visual demonstrations.A lever at the tool base controls gripper opening and closing, enabling intuitive interaction with everyday objects.
- 3.1 The DAT imitation framework: The reacher-grabber is attached to a 7DoF robot arm with a matching camera and mount, while a controllable mechanism actuates its fingers.The setup is designed to preserve the tool configuration between collection and execution.
- 3.2 Extracting actions from Visual Demonstrations: COLMAP-based Structure from Motion recovers the tool’s 6DoF motion, and a finger-detection network predicts gripper locations used to label open and close states.The detector is trained on 155 human-labeled frames, with states determined by whether finger distance crosses a threshold.
- 3.2 Extracting actions from Visual Demonstrations: The extracted observation-action pairs represent image observations and actions for moving from one observation to the next.The grasping action at a timestep is represented by the grasp state at the next timestep.
- 3.2 Extracting actions from Visual Demonstrations: The reconstructed action labels can be noisy, especially in lightly textured, clean, and high-dynamic-range scenes.The authors discard approximately 6% of aberrant demonstrations, while the finger detector achieves approximately 95% held-out accuracy.
- 3.3 Imitation from visual demonstrations: 1000 trajectories are collected for each of pushing and stacking, with diverse objects and scenes represented across the datasets.The figure shows individual trajectories alongside environmental variation.
- 3.3 Imitation from visual demonstrations: Behavior cloning maps a single 224 x 224 RGB image to a 3D translation vector and a continuous 6D rotation representation.The policy uses a CNN with fully connected layers, and the 6D rotation representation is selected for continuity in Euclidean space.
- 3.3 Imitation from visual demonstrations: Training experiments include color jitter, crops, cutout-color, rotations, and horizontal reflection to improve learning with limited data.Horizontal reflection also transforms the action labels.
4 Experiments
Experiments evaluate DemoAT on pushing and stacking using offline BC-MSE and real-robot success on unseen objects, while testing data scale, diversity, and augmentation. Larger and more diverse datasets, plus task-specific augmentations, improve learned-policy performance, with gains differing between offline and real-robot measures.
- Evaluation setup: DemoAT is evaluated on non-prehensile pushing and prehensile stacking using held-out BC-MSE and real-robot success on 24 unseen objects or configurations.BC-MSE compares predicted and ground-truth actions on novel objects and scenes, while robot evaluation measures successful executions.
- Behavioral cloning performance: 0.028 pushing BC-MSE and 0.056 stacking BC-MSE are achieved with full data and task-specific augmentations, versus random-action errors of 0.67 and 0.69.The selected augmentations are crop plus jitter for pushing and rotation plus jitter for stacking.
- Real-robot evaluation: 87.5% pushing and 62.5% stacking success rates are achieved on previously unseen objects with the learned policies.The real-robot evaluation shows substantial online performance despite the paper’s caution that offline BC-MSE does not necessarily predict execution quality.
- Data scale and diversity: 1000 demonstrations are collected for each task across multiple home and office environments with varied objects.The experiments test whether diverse demonstrations support generalization to previously unseen objects.
- Data scale and diversity: 0.9% BC-MSE improvement is observed when training data increases from 500 to 1000 trajectories, after larger gains in the low-data regime.Performance improvements diminish as more data is added.
- Data augmentation: 12.5% pushing and 33.4% stacking improvements occur in the second task stage with augmentation, and 50% augmented-data training surpasses 100% unaugmented training.Crop plus jitter is best for pushing, rotation plus jitter for stacking, while random color cutout performs less well.
5 Conclusion
DemoAT uses a universal reacher-grabber as both a demonstration interface and robot end-effector for visual imitation learning. The setup combines a simple human-side mount and camera with a robot-side actuator-equipped mount.
- DemoAT uses assistive tools to gather diverse data for robotic manipulation and support direct visual imitation learning.
- Users attach an angled mount and camera to the tool, then close its fingers by pressing a lever.
- The demonstration setup includes a reacher-grabber, camera, and 3D printed mount.
- The robot uses the same reacher-grabber attached to its end effector through a modified mount with a finger-control actuator.
C Visualizations of Predicted Actions
The visualizations compare predicted and reconstructed actions between consecutive camera frames for pushing and stacking. They show translations, rotations, and gripper-state predictions using arrows, color intensity, and bars.
- Aqua arrows show COLMAP ground-truth transverse actions, while yellow arrows show model predictions between consecutive frames.
- Color-map arrows encode the ground-truth and predicted up-down movements, with intensity representing action magnitude.
- Blue and green arrows represent true and predicted relative frame rotations, respectively.
- In stacking, bars show predicted probabilities for the gripper status at the next timestep, while green marks the true gripper label.
D Training Details
The policy network processes 224 x 224 RGB images through convolutional and fully connected layers to predict translation, rotation, and gripper-related actions. Training uses losses that include directional alignment.
- The architecture defines C_k as convolutional layers with k filters and F_k as fully connected layers of size k.
- An AlexNet-based convolutional encoder produces a latent image representation, followed by fully connected layers that predict relative translation.
- The network combines the latent representation with predicted translation before projecting relative rotation to a 6D vector.
- Training combines L1, L2, and direction losses because action direction matters more than magnitude.
- The model takes an image I_t ∈R3x224x224 and outputs Δp_t = (Δx_t, Δw_t) and gripper predictions.
E Third-person Views of Robot Experiments
Third-person views show additional robot trajectories for the learned pushing and stacking policies. Successful trajectories are green and unsuccessful trajectories are red, with task-specific execution failures identified.
- Pushing failures commonly occur when the gripper does not fully wrap around the object, allowing it to slide out during execution.
- Stacking failures commonly occur when the policy grasps too late or lifts the object insufficiently high.
- Successful robot trajectories are highlighted in green, while unsuccessful trajectories are highlighted in red.
F Closed-loop Control with Moving Objects
The learned closed-loop policies remain effective when objects or goals are perturbed during execution, adapting immediately to the changed scene.
- The policies successfully complete both pushing and stacking tasks after objects and goals are perturbed during online execution.Perturbation results are shown in Figure 11 and the supplementary video.
- Slightly moving the object or goal location prompts the policy to adapt immediately to the new scene.Frames with applied perturbations are highlighted in yellow.
G Study of Data Augmentations on Random Data Splits
On random, diverse data splits, augmentations improve performance across both tasks, while performance gains diminish as more training data are included.
- Performance gains from augmentation diminish as more data are included in the training set.This pattern appears for both random diverse and sequential data splits.
- Random diverse splits perform better than sequential splits across every fraction of data.The paper analyzes this difference further in Appendix H.
- Data augmentations improve performance for both tasks regardless of how random diverse data are split.The analysis uses random diverse splits rather than sequential splits.
H Diversity Size Analysis
Diverse data provides the largest performance advantage when only a small fraction of the data is used, and the gap narrows as dataset size increases.
- 1.4% increase in performance is observed for diverse dataset (B) versus sequential dataset (A) when using 10% of the data.Dataset (A) contains many observations of the same objects and scenes, whereas dataset (B) sparsely covers diverse objects and scenes.
- Diverse data produces the most prominent performance increase at 10% of the data, while the gap decreases as more data are added.The comparison covers error rates in both pushing and stacking.
- 0.005 increase in accuracy occurs in pushing at 75% data when using diverse rather than sequential data.
- 0.001 increase in accuracy occurs in stacking at 75% data when using diverse rather than sequential data.