PaperlayerView exact revision on arXiv

Source-linked AI summary

Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration

Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni, Sergey Levine

arXiv:1707.02920v2cs.LGcs.AIcs.RO

TL;DR

The paper addresses how inexpensive robots can learn multiple complex manipulation tasks from demonstrations using only raw images. It combines a shared recurrent controller with VAE-GAN reconstruction and autoregressive multimodal action prediction, and reports strong performance across five tasks, including success rates from 76% to 88%.

  • Problem

    Open-world and daily-living manipulation require vision-based control that can handle complex tasks on low-cost, imprecise robotic hardware.

    Source: §I · p. 1

  • Method

    The method trains one recurrent, parameter-shared controller from demonstrations, combining VAE-GAN image reconstruction with autoregressive multimodal action prediction.

    Source: §1 · p. 1, 2) VAE-GAN · p. 4

  • Results

    Across all five tasks, the full network outperformed the other architectural choices, achieving performance from 76% to 88%.

    Source: C. Results and Comparison of Network… · p. 7

  • Takeaways & Limitations

    Direct behavior cloning from raw images can teach a low-cost robot basic and longer manipulation tasks, including towel wiping and replacement.

    Source: §1 · p. 1, §V · p. 7

Abstract

from arXiv · show

We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks.

I. INTRODUCTION

The paper targets open-world manipulation with inexpensive, vision-only robots by learning multiple tasks directly from demonstrations. Its shared architecture combines task conditioning, multimodal action prediction, and image reconstruction, achieving complex manipulation through pure behavior cloning.

I. INTRODUCTION

  • The work addresses open-world and daily-living manipulation with low-cost, imprecise hardware using readily available camera images.The motivation emphasizes uncontrolled home environments and assistive tasks for elderly or disabled users.Source: §I · p. 1
  • The controller shares network parameters across multiple tasks and distinguishes them using a task-selector one-hot vector.The tasks include rigid, jointed, and deformable objects, while the robot relies on vision because it lacks proprioception.Source: §1 · p. 1
  • The experiments cover picking, placing, and non-prehensile manipulation with imprecise demonstrations on a low-cost manipulator.The controller also corrects mistakes and can attempt tasks multiple times.Source: §1 · p. 1
  • The authors report that the learned behaviors represent the most complex autonomous task execution successfully taught to this class of robotic arms using vision-based behavioral cloning.Source: §1 · p. 1
  • The approach learns complex skills such as towel pickup, object wiping, and towel replacement entirely from raw images using direct behavior cloning.The contribution is demonstrated on a low-cost robotic platform without dataset aggregation or reinforcement-learning refinement.Source: §1 · p. 1

II. RELATED WORK

Related work frames learned robotic control as a response to the difficulty of hand-engineering open-world behaviors. It positions learning from demonstration, end-to-end deep controllers, and parameter sharing as relevant routes toward efficient multi-task manipulation.

II. RELATED WORK

  • Open-world manipulation motivates learned controllers because execution depends on environmental circumstances and user preferences.Source: §II · p. 2
  • A major challenge in learning from demonstration is extending demonstrations to unseen situations, motivating larger or crowdsourced demonstration sets.Source: §II · p. 2
  • Learning from demonstration uses user-provided behavior, while reinforcement learning lets robots acquire skills independently but requires reward signals.For complex manipulation, reinforcement-learning rewards can be significantly delayed.Source: §II · p. 2
  • End-to-end controllers translate raw visual input into robot control signals through a single deep neural network.Prior examples combine visual features and robot state before predicting the next joint configuration.Source: §II · p. 2
  • Multi-task policies can condition on state and task, use task-specific and robot-specific modules, or share parameters across tasks.Prior work reports benefits from sharing parameters and jointly training the model.Source: §II · p. 2

III. LEARNING THE MULTI-TASK CONTROLLER

The method collects demonstrations for several tasks, trains one recurrent vision-based controller, and deploys it to convert camera observations into robot commands. Its visual representation combines VAE-GAN reconstruction with multimodal autoregressive prediction to handle varied demonstrations.

III. LEARNING THE MULTI-TASK CONTROLLER

  • The overall pipeline collects multi-task demonstrations, trains one deep recurrent network, and deploys it to transform raw camera perception into robot commands.Source: §III · p. 2
  • Human demonstrations are collected through hand-following teleoperation using either Leap Motion or Playstation Move hardware.The demonstration system and arm together cost less than $500.Source: A. Task Demonstration and Data… · pp. 2–3
  • Recorded demonstrations contain 128×128 RGB images at 33Hz, with trajectories down-sampled to 4Hz to create redundant offset trajectories.Source: A. Task Demonstration and Data… · p. 3
  • VAE-GAN processing maps input images to low-dimensional visual features while reconstructing realistic images through encoder, generator, and discriminator components.Source: B. Neural Network Architecture · p. 3
  • Multimodal autoregressive prediction captures distinct manipulation solutions without averaging them into potentially invalid motions.The estimator represents exponentially many modes with the number of autoregressive steps and uses Gaussian mixtures for expressive output distributions.Source: B. Neural Network Architecture · p. 3
  • The encoder is shared by the autoencoder and controller so reconstruction and control jointly shape the visual feature extractor.Latent sampling noise and dropout further regularize the recurrent controller.Source: B. Neural Network Architecture · p. 3

C. Training the Network

The controller is trained with a mixture-density error signal, while a VAE-GAN supplies a separate reconstruction-based signal. Its autoregressive factorization predicts robot joints sequentially using shared visual features and mixture-distribution parameters.

C. Training the Network

  • Training uses two error signals: an MDN-based controller loss and a VAE-GAN-based autoencoder loss.Source: C. Training the Network · p. 3
  • The controller factors the joint-configuration distribution into one-dimensional predictions ordered from base through gripper.Each joint is predicted autoregressively rather than all at once.Source: 1) Mixture Density Network · p. 3
  • The joint-action distribution is modeled as a mixture of Gaussian components whose number is controlled by the hyper-parameter m.The mixture coefficients, Gaussian means, and variances parameterize the distribution; diagonal variances are used instead of full covariance matrices.Source: 1) Mixture Density Network · pp. 3–4
  • At each time step, the current joint is predicted from encoded observation history and the network’s preceding joint predictions.Source: 1) Mixture Density Network · p. 3
  • The architecture shares an encoder between the controller and VAE-GAN autoencoder, producing features for both reconstruction and robot control.Source: Figure 2 · p. 4
  • Skip connections feed inputs to all LSTM layers, whose concatenated outputs produce m means, m variances, and m mixture coefficients.Softmax enforces mixture coefficients summing to one, while exponentiation enforces positive variances.Source: 1) Mixture Density Network · p. 4

2) VAE-GAN:

The VAE-GAN autoencoder regularizes representation learning by encoding images into latent variables, reconstructing them, and discriminating real from reconstructed images. Its objective combines prior, reconstruction, and adversarial errors.

  • The autoencoder uses an encoder, generator, and GAN discriminator to map images to latent representations, reconstruct them, and distinguish real from reconstructed images.Source: 2) VAE-GAN · p. 4
  • The discriminator evaluates whether an input image is real or reconstructed, providing the adversarial component of the training objective.Source: 2) VAE-GAN · p. 4
  • The latent representation is regularized toward a normal prior using KL divergence between q(z|x) and p(z).Source: 2) VAE-GAN · p. 4
  • The reconstruction error compares discriminator features from the original and reconstructed images with mean squared error.Source: 2) VAE-GAN · p. 4
  • The autoencoder error is the sum of prior, reconstruction, and GAN errors.Source: 2) VAE-GAN · p. 4

3) Implementation Details:

Training alternates between controller and autoencoder updates, then uses a two-step schedule that first trains the full network and later focuses on LSTM layers.

D. Executing the Policy at Test Time

At test time, the controller repeatedly observes images, predicts all joints autoregressively, executes the resulting action, and uses biased sampling to reduce repetition of demonstration mistakes.

E. Why this Architecture?

The architecture is designed for multimodal, temporally coherent manipulation: it models alternative solutions, preserves a selected strategy over time, handles visual occlusion, and conditions joint predictions autoregressively.

  • The paper empirically evaluates these architectural choices because they significantly impact performance.Source: E. Why this Architecture? · p. 5
  • Deterministic mean-squared-error behavior cloning can average distinct solutions, producing an invalid action such as a collision between left- and right-side avoidance paths.Source: E. Why this Architecture? · p. 5
  • The method models the full multimodal solution distribution and samples from it instead of averaging alternative demonstrations.Source: E. Why this Architecture? · p. 5
  • Recurrent memory helps the robot commit to one manipulation strategy rather than switching between alternatives at each time-step.Source: E. Why this Architecture? · p. 5
  • LSTM memory lets the controller continue acting when the object is temporarily occluded or inadequately represented by the visual encoder.Source: E. Why this Architecture? · p. 5
  • Autoregressive density estimation conditions each joint on previous joint predictions, coordinating actions such as end-effector positioning before gripper closure.Source: E. Why this Architecture? · p. 5

IV. EXPERIMENTS

The experiments evaluate five manipulation tasks on a fixed-base, low-cost six-axis arm using image-based demonstrations and compare reconstruction quality with task performance.

  • The evaluation uses a six-axis Lynxmotion AL5D arm with a two-finger gripper and a camera facing the robot.Source: §IV · p. 5
  • The five tasks manipulate objects with randomized initial positions and orientations within the robot arm’s reach.Source: A. Manipulation Tasks · p. 6
  • Three hours of demonstrations were collected per task, with 80% used for training and 20% for validation.Source: A. Manipulation Tasks · p. 6
  • The network compresses visual input into a 256-feature latent space and reconstructs images to check whether scene details are preserved.Source: B. Validating the autoencoder · p. 6
  • Table I reports each method’s percentile rate of successfully accomplishing the tasks.Source: Table I · p. 6
  • Reconstructed images generally preserve the objects and robot arm sufficiently for the LSTM to generate task trajectories.Source: B. Validating the autoencoder · p. 6

C. Results and Comparison of Network Architectures

The architecture study compares recurrent, multimodal, autoregressive, multi-task, and reconstruction-based choices. The full network performs best across all five tasks, while individual components address distinct generalization and robustness challenges.

V. CONCLUSIONS

The paper presents multi-task learning from user demonstrations as a way to control inexpensive robots for diverse manipulation tasks. Its five-task results support broader investigation of shared multi-task networks as more demonstrations accumulate.

  • The proposed approach learns multi-task manipulation from user demonstrations.Source: §V · p. 7
  • Images of the environment are mapped to the robot’s next joint configuration, supporting pushing, grasping, and longer towel-cleaning tasks.Source: §V · p. 7
  • The multi-task approach is more sample-efficient than the single-task approach because common patterns across tasks provide additional learning data.Source: §V · p. 7
  • The approach is demonstrated on five tasks, and the authors identify broader multi-task manipulation as a direction for future work.Source: §V · p. 7

Related papers

Loading 1707.02920v2…