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Language-Conditioned Imitation Learning for Robot Manipulation Tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, Heni Ben Amor
TL;DR
Robot imitation learning often lacks a communication channel for specifying task goals beyond motion and perceptual traces. This paper incorporates unstructured language into an end-to-end visuomotor policy for manipulation, achieving high success in simulation and credible performance with free-form human instructions, while requiring reviewed demonstrations and assuming a seven-degree-of-freedom robot in the stated setup.
Problem
Perceptual inputs and manually designed goal representations may not adequately specify target objects or motion constraints, and fixed specifications cannot be modified after deployment.
Method
The method jointly uses language, visual observations, and robot state through a high-level semantic module and a low-level controller trained with imitation learning.
Results
The model achieved 84% success on sequential simulated picking-and-pouring tasks and 64% success with free-form natural-language instructions from human users.
Takeaways & Limitations
Language-conditioned policies provide an intuitive interface for unstructured commands and significantly outperformed alternative methods while generalizing across experimental setups.
Takeaways & Limitations
The reported approach uses a seven-degree-of-freedom robot setup, and demonstrations and verbal descriptions were reviewed to mitigate misuse risks.
Abstract
from arXiv · showhide
Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., motion trajectories and perceptual data). No adequate communication channel exists between the human expert and the robot to describe critical aspects of the task, such as the properties of the target object or the intended shape of the motion. Motivated by insights into the human teaching process, we introduce a method for incorporating unstructured natural language into imitation learning. At training time, the expert can provide demonstrations along with verbal descriptions in order to describe the underlying intent (e.g., "go to the large green bowl"). The training process then interrelates these two modalities to encode the correlations between language, perception, and motion. The resulting language-conditioned visuomotor policies can be conditioned at runtime on new human commands and instructions, which allows for more fine-grained control over the trained policies while also reducing situational ambiguity. We demonstrate in a set of simulation experiments how our approach can learn language-conditioned manipulation policies for a seven-degree-of-freedom robot arm and compare the results to a variety of alternative methods.
1 Introduction
The paper introduces language as a flexible goal specification for imitation learning, combining language, vision, and control to guide robot manipulation. In simulation, the model performs picking and pouring tasks from both synthetic and free-form human instructions.
- Motivation: Perceptual inputs alone may not specify optimal actions, while manually designed goal representations are inflexible and fixed before training.Goals can describe target objects or motion constraints, such as execution speed or end-effector acceleration.
- Language-conditioned setting: Language provides a flexible interface for specifying object properties and motion constraints in manipulation tasks.Examples include selecting objects by color and directing how much content to pour.
- Approach: The proposed end-to-end architecture combines a high-level semantic network with a lower-level controller to map visual observations and language goals to motor control.The semantic network encodes language, visual observations, and current joint positions; the controller generates suitable control policies.
- Results: 84% success was achieved on sequential simulated tasks requiring a robot to pick up a cup and pour its contents into another vessel.The model outperformed state-of-the-art baselines and was also evaluated for modifier generalization and robustness to visual and physical perturbations.
- Results: 64% success was achieved with free-form natural-language instructions from human users, despite training on synthetic language.The experiments used a dynamic-enabled simulator with random object assortments and procedurally generated instructions.
2 Background
Prior imitation-learning methods can teach robot skills from demonstrations, but they depend on carefully designed state representations and often assume existing task taxonomies or motion primitives.
- Imitation learning: Imitation learning converts human demonstrations into functional or probabilistic representations for teaching agents new skills.Demonstrations can be collected through human-provided examples rather than conventional programming.
- Limitations: Successful adaptation requires a carefully designed state representation containing all information needed by the learned policy.The background identifies this representation requirement as a limitation of existing approaches.
- Limitations: Existing approaches commonly assume that a sufficiently large task taxonomy or set of motion primitives is already available.Under this assumption, semantics and motions are not trained jointly.
3 Problem formulation and approach
The approach learns an end-to-end policy that combines natural-language task descriptions, visual observations, and robot state to produce language-conditioned motor control. Its semantic model identifies relevant objects and task information, while a closed-loop controller generates and updates motor primitives during execution.
- 3 Problem formulation and approach: The policy objective maps natural-language task descriptions and perceptual data to imitated robot behavior, assuming a seven-degree-of-freedom robot with state trajectories over time.Each demonstration contains robot states, perceptual data, and a natural-language task description.
- 3.2 Semantic model: The semantic model encodes the command with a GRU, scores candidate objects using language-conditioned attention, and combines attended object features with the sentence embedding.The resulting task embedding preserves command information needed for modifiers such as “everything” or “some.”
- 3.3 Control model: The control model maps the task embedding and current robot state to motor-primitive parameters, including trajectory weights, phase, and phase progression.Motor primitives represent trajectories across all robot degrees of freedom using radial basis function kernels.
- 3.3 Control model: The controller executes the generated trajectory by evaluating radial basis functions at the updated phase and computing the next joint configuration through a sparse linear map.The closed-loop controller recalculates primitive parameters at each time step to account for perturbations and execution noise.
- 3.4 Model integration: The integrated model sequentially preprocesses language and vision, creates a task embedding, and translates it with the robot state into motor-primitive hyperparameters.This connects semantic interpretation directly to time-varying control generation.
- 3.5 End-to-end training: End-to-end training uses a weighted sum of five auxiliary losses covering attention, task-related supervision, phase, motor-primitive weights, and phase progression.The attention loss is a multiclass cross-entropy objective over candidate objects, while controller guidance includes mean-squared-error losses.
- 3.5 End-to-end training: Figure 2 presents the simulated object set alongside example execution sequences for picking and pouring commands.The figure links available scene objects with the two task types used in the simulation setup.
4 Evaluation and results
The evaluation tests language-conditioned imitation learning in simulated picking and pouring tasks, including ablations, new users, perturbations, and baseline comparisons. The model achieves strong task success but performance varies with linguistic complexity and unseen commands.
- Basic metrics: 84% overall task success was achieved across 100 novel environments, with 98% for picking and 85% for pouring.Overall success required lifting a cup and pouring its contents into the correct bowl.
- Language and visual feature combinations: Success decreased to about 70% when the target bowl required both shape and size, despite individual-feature success rates of 100% for shape and 85% for size.The remaining feature combinations reflected the general 85% success rate for pouring.
- Generalization to new users and perturbations: 64% of complete task sequences succeeded for commands from four new users, while picking remained at 93% and pouring declined from 85% to 69%.The model nearly doubled its trajectory error rate on new language commands.
- Generalization to perturbations: Figure 3 evaluates the same model under physical perturbations, visual perturbations, and verbal disambiguation.The physical experiment applies a force during the path, while the visual experiment changes environmental appearance and checks object detection.
- Baseline comparison: Table 3 compares the proposed model with an LSTM feature baseline and PayAttention! on pouring, picking, and sequential tasks under closed-loop and ground-truth conditions.The baselines largely failed on the sequential task, while PayAttention! had difficulty identifying targets that required more than noun-based differentiation.
- Model ablations: The full loss function reached 84% success, compared with 51% using attention plus trajectory loss and about 28% using trajectory loss alone.Adding controller losses without attention produced 0% task completion, while attention aligned detected objects with the verbal object clause.
- Model ablations: Replacing the attention mechanism with a feed-forward network caused a severe performance decline.The modified network combined all image features with the sentence embedding without attention.
5 Conclusion
The paper concludes that end-to-end imitation learning can combine language, vision, and control to provide intuitive natural-language interfaces for robot manipulation. It reports strong comparative performance and generalization across setups and unseen users, while allowing newer vision and language models to replace the chosen components.
- Language-conditioned policies provide a simple interface for human users to issue unstructured commands.
- The approach significantly outperformed alternative methods and generalized across experimental setups and free-form instructions from previously unseen users.
- The framework is independent of its FRCNN perception and GloVe language-embedding choices, allowing newer models such as BERT to replace them.
6 Broader impact
The paper discusses natural-language robot instruction as a route toward broader applications in accessibility, elder care, and small and medium-sized enterprises. It also highlights safety and misuse risks when learned policies control physical manipulators.
- Natural-language control could support applications such as autonomous wheelchairs and may improve quality of life for people with disabilities.
- Verbal instructions could open new machine-learning and robotics applications while improving transparency and reducing technological anxiety.
- Robots with verbal instructions may help SMEs avoid economically unsustainable repeated programming for small product batches.
- Learning from demonstrations and explanations introduces risks of nefarious use and adversarial attacks because the system controls a physical manipulator.The work reviewed demonstrations and descriptions and used attention to let users verify the selected target object.