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Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

Tianhao Zhang, Zoe McCarthy, Owen Jow, Dennis Lee, Xi Chen, Ken Goldberg, Pieter Abbeel

arXiv:1710.04615v2cs.LGcs.RO

TL;DR

Collecting high-quality demonstrations for pixel-to-action visuomotor policies remains difficult in robotic manipulation. This paper uses consumer-grade VR teleoperation to gather demonstrations and trains deep imitation-learning policies, which achieve high success rates and generalize across real-world manipulation tasks with under 30 minutes of demonstrations per task.

  • Problem

    High-quality demonstrations for learning pixel-to-action visuomotor policies remain difficult to collect for robotic manipulation.

  • Method

    A consumer-grade VR teleoperation system lets humans naturally control a PR2 robot while sharing its observation and action spaces, producing demonstrations for deep visuomotor imitation learning.

  • Results

    With under 30 minutes of demonstrations per task, learned policies achieved high success rates, generalized to novel situations, and completed long-running manipulation sequences.

  • Takeaways & Limitations

    Simple imitation learning can train data-efficient pixel-to-action policies for a range of complex real-world manipulation tasks.

  • Takeaways & Limitations

    Although the policies often succeeded, their behavior was frequently suboptimal, including slow movement, pauses, non-shortest paths, and repeated grasp attempts.

Abstract

from arXiv · show

Imitation learning is a powerful paradigm for robot skill acquisition. However, obtaining demonstrations suitable for learning a policy that maps from raw pixels to actions can be challenging. In this paper we describe how consumer-grade Virtual Reality headsets and hand tracking hardware can be used to naturally teleoperate robots to perform complex tasks. We also describe how imitation learning can learn deep neural network policies (mapping from pixels to actions) that can acquire the demonstrated skills. Our experiments showcase the effectiveness of our approach for learning visuomotor skills.

I. INTRODUCTION

The paper presents an inexpensive consumer-grade VR system for teleoperating a PR2 robot and collecting high-quality demonstrations for manipulation. Using these demonstrations, behavioral cloning learns visuomotor policies across ten real-world tasks, with less than 30 minutes of data per task.

  • I. INTRODUCTION: High-quality manipulation demonstrations are difficult to obtain because kinesthetic teaching is challenging, while trajectory optimization and reinforcement learning require carefully designed rewards, dynamics models, and substantial robot interaction.Existing specialized teleoperation systems can simplify collection but may be expensive and tailored to specialized hardware.
  • I. INTRODUCTION: The study collected demonstrations for ten real-world PR2 manipulation tasks and trained deep visuomotor policies with behavioral cloning, which the authors found surprisingly effective given high-quality demonstrations.The policies directly map pixels to actions using a simple imitation-learning method.
  • I. INTRODUCTION: The system uses consumer-grade VR devices to teleoperate a real PR2 robot, letting operators perceive the robot’s sensor space and control it with motion-tracked VR controllers.This approach targets intuitive manipulation and high-quality demonstration collection without specialized teleoperation hardware.
  • I. INTRODUCTION: A single neural-network architecture maps raw color and depth pixels to actions for all tasks, with auxiliary prediction connections added to accelerate learning.The architecture is shown in Fig. 3.
  • I. INTRODUCTION: Less than 30 minutes of demonstrations per task was sufficient to learn successful policies using identical hyperparameters and neural-network architecture across tasks.The authors describe this as their most surprising finding.

II . R E L AT E D W O R K

Related work distinguishes behavioral cloning from inverse reinforcement learning and motivates learning policies directly from raw pixels. The paper focuses on behavioral cloning with Virtual Reality teleoperation to address challenges in collecting visual demonstrations for real-world manipulation.

  • Related Work: Behavioral cloning learns observations-to-actions through supervised learning, whereas inverse reinforcement learning estimates a reward function explaining demonstrations as near-optimal behavior.This work focuses on behavioral cloning.
  • Related Work: Behavioral cloning succeeds in robotics with low-dimensional state representations, while raw-pixel policies have succeeded in simulation, driving, and drones, [5],,, [35],.Learning directly from pixels is desirable because extracting state information is often challenging.
  • Related Work: Real-world manipulation demonstrations are difficult to collect: kinesthetic teaching is unintuitive and motion-capture teleoperation can create viewpoint correspondence problems,, [37].Virtual Reality teleoperation provides a direct observation-action mapping and leverages natural human manipulation instincts.
  • Related Work: Reinforcement learning can learn pixel-to-action policies, but real-world exploration is often impractical, and reward specification is difficult,,,.Guided Policy Search reduces sample complexity but relies on trajectory-centric reinforcement learning to obtain demonstrations autonomously.

III. VIRTUAL REALITY TELEOPERATION … C. Control Interface

The system uses consumer-grade VR teleoperation to let humans naturally generate demonstrations for pixel-based policy learning. Its visual and control interfaces align the operator’s movements with robot perception and gripper motion while addressing motion sickness and kinematic mismatch.

  • III. VIRTUAL REALITY TELEOPERATION: The teleoperation system is intended to let humans naturally produce demonstrations suitable for learning policies that map pixels to actions.
  • A. Hardware: The teleoperation platform combines a $600 Vive VR system, a PR2 robot, and a low-cost 3D camera for visual sensing.The Vive provides 6 DoF hand-controller tracking at sub-millimeter precision and 90 Hz, while the camera supplies first-person color and depth images at 30 Hz.
  • B. Visual Interface: The visual environment is designed to be informative, intuitive, and comfortable for extended teleoperation by leveraging operators’ intuition about 3D space.
  • B. Visual Interface: Directly displaying stereo images can cause motion sickness and introduce lag because the PR2 head has fewer degrees of freedom and lower precision than a human head.Matching a 6D human head pose may require the PR2 base and torso to move, increasing inconsistency between head movements and the displayed scene.
  • B. Visual Interface: An RGB-D camera and rendered colored 3D point cloud provide the virtual scene, while a virtual camera updates instantaneously with the operator’s head pose to avoid motion sickness.
  • C. Control Interface: The system uses Vive hand controllers to control the robot arms, with a trigger button commanding the gripper to fully open or close.In a unified virtual coordinate frame, the tracked operator-hand pose becomes the target pose of the corresponding robot gripper.
  • C. Control Interface: The control mechanism is natural because moving a hand moves the robot gripper’s pose target correspondingly, enabling first-time users to perform complex manipulation tasks.It minimizes kinematic differences from natural movement compared with kinesthetic teaching and provides instantaneous feedback to the operator.

IV. LEARNING · A. Neural Network Control Policies

The paper learns neural network control policies through behavioral cloning from observation–control demonstrations. Policies use visual, depth, and recent end-effector information to predict right-arm velocities and gripper commands, with an architecture augmented by depth input and auxiliary tasks.

  • IV. LEARNING: Behavioral cloning trains a policy to reconstruct controls from observation–control pairs collected across multiple demonstrations for each task.The dataset contains example observation–control pairs, and πθ(ut|ot) learns the corresponding mapping.
  • A. Neural Network Control Policies: Each policy input combines a 160×120×3 RGB image, the current depth image, and three end-effector points from the five most recent steps.RGB and depth are collected by the onboard 3D camera, while the point history represents the right-arm end-effector state.
  • A. Neural Network Control Policies: The five-step end-effector history lets the robot infer velocity and acceleration from kinematic state.Joint angles are excluded because the human operator directly controls the end effector’s six degrees of freedom rather than the seven arm joints.
  • A. Neural Network Control Policies: The policy outputs right-hand angular velocity, linear velocity, and, for grasping tasks, a binary desired gripper state.Although the platform supports both arms and the head, only the right arm is controlled; other joints remain frozen except during resets.
  • A. Neural Network Control Policies: During execution, the policy generates controls from the current observation as ut = πθ(ot).The control vector includes the right-hand motion commands and the gripper command when applicable.
  • A. Neural Network Control Policies: The architecture follows while adding depth images and auxiliary prediction tasks intended to accelerate learning.Its modules separate vision, auxiliary prediction, and control components, with image features extracted by a convolutional network and spatial soft-argmax layer.

B. Loss Functions

The experiments use a modified behavioral-cloning loss that combines standard fitting objectives with directional alignment and task-specific auxiliary predictions. Grasping tasks additionally predict gripper state using sigmoid cross entropy.

  • B. Loss Functions: Behavioral cloning fits demonstration pairs using standard l1 and l2 losses, with a small modification for these experiments.
  • B. Loss Functions: A directional-alignment loss encourages the predicted and demonstrated end-effector movements to point in similar directions rather than match only in magnitude.This reflects the emphasis on movement direction over magnitude; the arccos outputs range from 0 to π.
  • B. Loss Functions: For grasping tasks, the final layer predicts gripper open/close with a scalar logit trained using sigmoid cross entropy.The target gripper state is binary, gt ∈{0, 1}.
  • B. Loss Functions: The overall objective is a weighted combination of standard losses and auxiliary prediction losses.Auxiliary prediction tasks are described in Section IV-C.
  • B. Loss Functions: The neural network policies are trained with stochastic gradient descent on randomly sampled batches from Dtask.

C. Auxiliary Loss Functions · V. EXPERIMENTS

The method adds concurrently trained auxiliary pose-prediction losses using labels inferred from task demonstrations, while experiments evaluate VR-collected demonstrations on real PR2 manipulation tasks. With under 30 minutes of demonstrations per task, learned policies achieve high success rates and good generalization.

  • C. Auxiliary Loss Functions: Auxiliary prediction tasks provide an additional source of self-supervision, building on evidence that self-supervisory signals can improve data efficiency and robustness [51].Each task adds a small two-layer fully connected module after the spatial soft-argmax layer.
  • C. Auxiliary Loss Functions: Each auxiliary module is trained with an L2 loss to predict its corresponding label.The prediction is denoted ŝ^(a), with the target label denoted s^(a).
  • C. Auxiliary Loss Functions: Auxiliary labels can be inferred directly from the task dataset, including the current gripper pose p_t and final gripper pose p_T.These labels are available from the demonstrations rather than requiring separate annotation data.
  • C. Auxiliary Loss Functions: Unlike prior pose-based pretraining with a separate labeled image dataset, this approach trains the auxiliary objectives concurrently without additional data.The comparison concerns the data source and training procedure for pose-related supervision.
  • V. EXPERIMENTS: The experiments examine policy success across challenging manipulation tasks, sample complexity, and whether auxiliary prediction losses improve real-world data efficiency.These questions are evaluated using demonstrations collected through the proposed VR teleoperation system.
  • V. EXPERIMENTS: With under 30 minutes of demonstrations per task, learned policies achieve high success rates and good generalization on real PR2 manipulation tasks.The experiments investigate whether VR teleoperation can produce successful deep visuomotor policies with little tuning.

A. Experimental Setup

The experiments evaluate VR-teleoperated imitation learning on diverse manipulation tasks requiring localization, precision, contact handling, deformable-object management, and long-horizon generalization. Demonstrations were collected in single sessions, with human operators providing sufficient local variation without explicitly minimizing demonstration counts.

  • A. Experimental Setup: The evaluation covers ten challenging tasks, including reaching, grasping, pushing, assembly, insertion, alignment, sequential placement, and cloth manipulation.These tasks span bottles, tools, toy blocks, planes, fruits, bowls, and disheveled cloth.
  • A. Experimental Setup: Successful policies must combine object localization, high-precision control, contact handling, deformable-object manipulation, and generalization.Tasks requiring multiple movement stages and longer durations were included to test vulnerability to compounding errors in long-horizon imitation learning.
  • A. Experimental Setup: Demonstrations for every task were collected with the VR teleoperation system, typically during a single robot-interaction session.The study validated feasibility rather than searching explicitly for the minimum demonstrations needed for successful policies.
  • A. Experimental Setup: Human-operated demonstrations naturally provided sufficient local training variation, complementing the number of collected samples.This contrasts with prior approaches such as GPS, which use linear Gaussian controllers to inject desired noise.

B. Results and Analysis

With fewer than 30 minutes of demonstrations per task and fixed hyperparameters, learned policies achieved high success rates and generalized to unseen test states across challenging manipulation tasks. Analyses further examined skill quality, extrapolation, sample complexity, and auxiliary losses, while identifying suboptimal behaviors and failure modes.

  • Results: With under 30 minutes of demonstrations per task, all learned policies achieved high success rates and good generalization to unseen test situations across the evaluated manipulation tasks.Policies were trained with a fixed neural-network architecture and hyperparameters, then evaluated from unseen initial states with randomized object-position variations within the training regime.
  • Skill Quality: The policies learned complex skill execution, including balancing a block with one contact, adaptive insertion behavior, and transitions between maneuvers in long-running tasks.Successful evaluation trials included pushing and the repeated grasp-place-x2 sequence.
  • Limitations: Despite meeting the success metric, policies were often suboptimal, taking inefficient paths, moving slowly, pausing, retrying grasps, or exhibiting task-specific failures.Reported failures included knocking over objects, missed grasps or alignments, boundary trapping, and stopping near targets.
  • Extrapolation: Policies extrapolated beyond training states by rejecting an unseen competing bottle, succeeding with a block 10 cm lower, and handling a hammer 6 cm farther away.These tests probed generalization limits using initial states outside the demonstrated training regime.
  • Sample Complexity: Reducing demonstration subsets for the nail and grasp-and-place tasks enabled direct evaluation of the method’s sample-complexity limits using the same test initial states.The resulting performance comparisons are reported in Table II.
  • Auxiliary Prediction Loss: Auxiliary prediction losses empirically improved data efficiency for grasp-and-place policies trained with varying amounts of demonstrations.The comparison used policies trained with and without auxiliary losses; the motivation was prior evidence from simulated games [51].

VI . C O N C L U S I O N

The paper presents VR teleoperation as a practical way to collect high-quality demonstrations for visuomotor learning, showing that imitation learning can learn pixel-to-action policies from limited data. It also identifies future directions for richer VR feedback, policy diagnosis, and bimanual manipulation.

  • Contributions: VR teleoperation enables easy collection of high-quality robotic manipulation demonstrations suitable for visuomotor learning.The system uses consumer-oriented VR interaction to support natural demonstration collection.
  • Contributions: Imitation learning can effectively train deep policies mapping RGB-D images directly to actions with only a small amount of learning data.The conclusion highlights this result as a central finding of the paper.
  • Future Work: Future VR extensions include visual policy diagnosis, human-provided control-variance signals, and richer demonstrator feedback through haptics and sound.These additions would further exploit VR beyond natural demonstration collection.
  • Future Work: Because the system controls the robot’s head and both arms, future work could investigate policies for bimanual manipulation.The conclusion presents bimanual learning as an open direction enabled by the current control setup.

APPENDIX · A. Task Specification

The appendix defines ten robot manipulation tasks with varied object placements, gripper initializations, action sequences, and task-specific success criteria. The tasks range from single-step reaching and insertion to long-horizon, visually diverse manipulation under unseen test configurations.

  • A. Task Specification: Reaching tests grasping a randomly positioned bottle within a 30 x 50 cm region while avoiding knocks, with success assessed after manually closing the gripper.The gripper starts from a fixed pose, and the bottle must be graspable at trial end.
  • A. Task Specification: Grasping requires acquiring a randomly positioned, variably oriented toy hammer from a 15 x 15 cm area without dropping it when the gripper is moved.The gripper starts pointing 45° upward, 45° downward, or horizontally toward the center.
  • A. Task Specification: Pushing requires directing a closed gripper to move a randomly positioned LEGO block into a fixed zone while maintaining contact and pushing direction.The block varies in position and orientation within a 40 x 20 cm area, and the gripper starts at three positions.
  • A. Task Specification: Plane assembly requires precise alignment to insert both the peg and rectangular wheel base, with plane and wheels initialized across regions and orientations.Success means the wheels are fully inserted and cannot be moved.
  • A. Task Specification: Cube insertion and nail alignment test generalization from demonstrated initial states to midpoint configurations, requiring insertion into a matching slot or positioning hammer claws beneath a nail head.The cube uses six training states and midpoint test states, while the nail uses three demonstrated gripper poses and analogous midpoint resets.
  • A. Task Specification: Grasp-and-place requires grasping and lifting an apple before placing it on a fixed plate, where incorrect lifting can shift the plate.Success requires the apple to be within the plate and the gripper open at the end.
  • A. Task Specification: Grasp-drop-push chains grasping and lifting an apple, dropping it into a bowl, and pushing the bowl beside a fixed cup across randomized object positions.The long sequence also requires three-dimensional awareness for gently dropping the apple into the bowl.
  • A. Task Specification: Grasp-Place-x2 requires consecutively carrying an orange and an apple to separate targets, while Cloth requires lifting a visually diverse cloth from trained regions and an unseen intermediate test location.The fruit task is long-duration and vulnerable to rolling, whereas cloth shapes and pile heights vary substantially.

B. Loss Functions · C. Neural Network Policy · AC KNOWLE DGME NT

The experiments used fixed loss weights and auxiliary gripper-pose prediction objectives, while policies followed a specified initialization and ADAM optimization setup. The work acknowledges contributors, funding programs, and fellowship support.

  • B. Loss Functions: Vision networks predicted the current gripper pose and final-time gripper pose through auxiliary losses.The pose was represented by three points on the end effector.
  • B. Loss Functions: For plane and cube tasks, vision networks additionally predicted the current pose of the left gripper used to hold an object.
  • C. Neural Network Policy: Policies used the architecture described in Section IV-A, with parameters initialized uniformly from [-0.01, 0.01] except RGB first-layer filters initialized from GoogLeNet trained on ImageNet.
  • C. Neural Network Policy: Policy optimization used ADAM with its default learning rate of 0.001 and batch size 64.
  • AC KNOWLE DGME NT: The authors acknowledge writing and supplementary-video assistance and report funding from DARPA Simplex, ONR PECASE, and the Berkeley Deep Drive consortium.Tianhao Zhang, Zoe McCarthy, and other contributors also received fellowship support.
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