On Designing a Learning Robot:

Improving Morphology for Enhanced Task Performance and Learning

Maks Sorokin 1*,2    Chuyuan Fu1    Jie Tan 3    C. Karen Liu 4
Yunfei Bai 5   Wenlong Lu1 Sehoon Ha 2   Mohi Khansari1

2Georgia Tech
3Robotics at Google
4Stanford University
5 Work done while at Everyday Robots.
* The research was conducted during PhD Residency at Everyday Robots.
Read the paper link
Preprint 2023 (arXiv)


As robots become more prevalent, optimizing their design for better performance and efficiency is becoming increasingly important. However, current robot design practices overlook the impact of perception and design choices on a robot's learning capabilities. To address this gap, we propose a comprehensive methodology that accounts for the interplay between the robot's perception, hardware characteristics, and task requirements. Our approach optimizes the robot's morphology holistically, leading to improved learning and task execution proficiency. To achieve this, we introduce a Morphology-AGnostIc Controller (MAGIC), which helps with the rapid assessment of different robot designs. The MAGIC policy is efficiently trained through a novel PRIvileged Single-stage learning via latent alignMent (PRISM) framework, which also encourages behaviors that are typical of robot onboard observation. Our simulation-based results demonstrate that morphologies optimized holistically improve the robot performance by 15-20% on various manipulation tasks, and require 25x less data to match human-expert made morphology performance. In summary, our work contributes to the growing trend of learning-based approaches in robotics and emphasizes the potential in designing robots that facilitate better learning.



  Learning has been one of the most promising tools for operating robots in unstructured environments, enabling them to acquire complex perception and reasoning capabilities. However, the current status quo of designing robots does not account for the impact of learning: rather, many robots are still designed based on human experts' intuition or hand-crafted heuristics. Therefore, such designs can lead to a sub-optimal performance by causing unexpected visual occlusions. This is where the idea of guiding robot design to improve the robot learning capability comes in, inspired by the evolutionary process.

Figure 1. - Examples of naturally occurring object and workspace occlusions due to the pose and configuration of the robot.

  This work particularly aims to discuss the design optimization for vision-based manipulation. In the general context of manipulation, visual sensors provide a rich stream of information that allow robots to perform tasks such as grasping, object manipulation, and assembly. The use of visual sensors in manipulation, however, inevitably poses challenges associated with complete or partial field-of-view occlusion, which can degrade performance due to limited perception (Fig. 1). Whether an occlusion is harmless or fatal often depends on the task at hand and the stage of task execution. Such scenarios motivate us to explore hardware optimization without neglecting the interplay between the robot's morphology, onboard perception abilities, and their interaction in different tasks.

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Figure 2. A side-by-side comparison of existing (left) vs proposed (right) hardware design optimization approaches.

  Unlike typical two-loop morphology optimization frameworks (Fig. 2 - left), our key idea is to leverage the morphology-agnostic policy $\pi^\triangle$ trained only once. Once the morphology-agnostic controller is learned, we optimize robot design parameters using Vizier optimizer using the controller's performance as a surrogate measure (Fig. 2 - right). Such an approach provides a more accurate evaluation of observability and reachability in terms of "learnability", and it is substantially more feasible than per-design training.

Morphology-Agnostic Controller
Figure 3. Morphology-AGnostIc Controller (MAGIC) policy is capable of controlling a range of morphologies using onboard visual observations, greatly reducing the costs of traditional two-loop design optimization. (left) onboard camera observations that are used to generate robot action. (right) top camera view of the robot performing the task

  Morphology-Agnostic Controller $\pi^\triangle$ (Fig. 3) is a neural network policy trained to control a wide range of robot morphologies using the onboard camera sensing. The policy $\pi^\triangle$ through learning is primed to disregard occlusions from onboard observation to generate suitable actions. Hence, if the $\pi^\triangle$ is successful, then the information is sufficient, meaning that the morphology does not cause any major occlusions that prevent the task success. The policy $\pi^\triangle$ is used to measure a surrogate performance metric, helping to rapidly assess the quality of different morphologies.

Priveleged single-stage learning framework
Pipeline architecture
Figure 4. Morphology-AGnostIc Controller (MAGIC) trained via PRIvileged Single-stage learning via latent alignMent (PRISM) (hover over   #   to expand)

  To train MAGIC, we introduce PRIvileged Single-stage learning via latent alignMent (PRISM), which unifies the traditional two-stage approach into single-stage by using a latent space alignment loss during the optimization process. Fig. 4 summarizes the overall architecture of PRISM.

"supernatural" (privileged) motion
realistic (onboard sensor) behavior
Figure 6. Motivating illustration for PRIvileged Single-stage learning via latent alignMent (PRISM) (left) - supernatural robot motion enabled through use of privileged information. (right) - realistic robot motion, characteristic of onboard sensing, enabled via single-stage privileged learning using PRISM framework.

  Single-stage unification allows us to discourage the student policy from learning behaviors which incorrectly exploit the information only present in privileged states (Fig. 5 - left) which is important to learn realistic vision-based policies (Fig. 5 - right). To make this approach possible, we use a combination of loss functions to train the policy network. We use Behavioral Cloning for action and explicitly align information in the encoder latent space.

Figure 7. Manipulation task examples. (left) - onboard camera (first person view) video. (right) - top/side/back view camera video.

  We use reaching and manipulation tasks for training the policy. The reaching task involves moving the end-effector to a specific goal position, while the manipulation task involves picking an object, placing it in a cabinet, and closing the cabinet door (Fig. 7).

Robot Design Examples
Figure 8. Examples of robot morphologies used in data collection. During collection each morphology link lenghts are sampled uniformly at random from link specific ranges.

  The data collection process involves initializing a morphology with uniformly sampled link lengths (Fig. 8) and collecting trajectories using a motion planner (IK with collision avoidance). We use PyBullet simulator to collect the demonstration data. In total, we collect 500k successful trajectories for training the MAGIC policy.

Hardware Optimization
Figure 9. Single loop optimization using MAGIC policy as a surrogate evaluator.

  The objective function for the optimization is the success rate of the morphology-agnostic policy $\pi^\triangle$ on the task of reaching and/or manipulation. The success rate of $\pi^\triangle$ serves as a surrogate measure of the robot morphology quality. We use a sampling-based optimization algorithm Vizier, to optimize robot design parameters, to efficiently search for the optimal solution among a large number of possible morphologies (Fig. 9)

Targeted policy

  A targeted policy $\pi^T$ is a controller designed to control a specific robot, unlike a MAGIC $\pi^\triangle$ policy, which is designed to perform well across multiple robots. We use targeted policy as an ablation to evaluate how much the performance of our proposed MAGIC policy $\pi^\triangle$ is compromised to accommodate for multi-morphology capability. We train the targeted policy $\pi^T$ using PRISM but with a fixed robot design. For a particular robot, we collect $100$k successful targeted demonstrations while keeping morphology $m_i$ intact during the data collection process. We use targeted demonstration to train the targeted policy $\pi^T$ from scratch until convergence.

The results of our experimentation are structured to answer the following questions:
  1. Does a morphology-agnostic policy provide a good surrogate of a true performance?
  2. How does the MAGIC-optimized design compare to a human-expert design?
  3. How does incorporating onboard sensing affects the morphology optimization process?
  4. What effects do additional robot tasks have on the optimized robot morphology?
Targeted vs. Morphology-Agnostic Policies
Figure 10. Targeted vs. Morphology-Agnostic Policies. MAGIC $\pi^\triangle$ and targeted $\pi^T$ policies have a 14.44% performance gap on the human-expert design, which shrinks to 1% on the MAGIC-optimized design. We omit the targeted performance evaluation on random morphologies due to computational cost.

  We report the performance of the morphology-agnostic controller $\pi^\triangle$ and targeted controller $\pi^T$ on a human-expert robot design $m^H$ and the best MAGIC-optimized solution candidate $m^\star$ (Fig. 10). We find that the reaching task performance of the $\pi^\triangle$ and $\pi^T$ is nearly identical for $m^H$ (difference: <1%). On the other hand, the performance on $m^\star$ design has a gap of 14.44%. We hypothesize that $m^H$ causes less occlusion and hence is easier to control using $\pi^\triangle$ compared to $m^H$. However, policy $\pi^T$; can learn to exploit the structure of occluding arm to infer the missing information, such as a rough pose of an end-effector, through targeted re-training.

Comparison to a human-expert design
Figure 11. Robot Performance & Learning Efficiency. (a) Learning oriented morphology design outperforms the human-expert design by 18.95% when trained with the same amount of data. (b) Learning oriented design can achieve the same performance as human-expert design using 25x less data.

  Next, we analyze the difference between MAGIC-optimized $m^\star$ and human-expert $m^H$ designs in terms of task performance and learning efficiency (Fig. 11). We highlight the absolute performance improvement of targeted policies when evaluated on $m^\star$ compared to $m^H$ on the task of reaching. Specifically, $m^\star$ achieves success rate of 80.19% compared to 61.24% with $m^H$.
  The performance gain on $m^\star$ could be attributed to a reduced frequency of sensor occlusions. We hypothesize that the reduced distortion of onboard information can also result in a higher quality of data during collection, which naturally leads to improved robot learning capabilities. To measure the learning efficiency, we compare the success rates of the policies trained with a varying number of demonstrations. Fig. 11 shows that $m^\star$ is a better-suited robot for learning compared to $m^H$, as it requires 25x less data than $m^H$ to reach the same performance when training the controller from scratch.

Significance of onboard sensing
Figure 12. Onboard and privileged design objective comparison. Morphology optimized using privileged objective has a major performance drop when evaluated using onboard information, emphasizing the importance of evaluation with onboard sensing.

  Next, we seek to investigate the significance of onboard sensing during design optimization. If the robot policy is given access to all of the information present in the environment, a robot may be able to reach its upper-bound performance, which is mostly constrained by kinematic and dynamic capabilities. However, it might perform suboptimally when tested with onboard sensing because the morphology can limit its sensing capabilities via visual occlusions.
  We use a privileged controller $\pi_P^\triangle$ during the morphology optimization phase, which acts as an optimal motion planner with the ground-truth robot and task information. Once the morphology is found, we evaluate its performance with the policy which relies on onboard information $\pi_R^\triangle$. In Fig. 12, we compare the performance of privileged information morphology and onboard sensing morphology $m^\star$. We observe that if we only use the privileged policy during the morphology design, there is a significant 84% drop in performance when the robot is evaluated with onboard sensing. In contrast, $m^\star$ performs well in both onboard and privileged settings.

Multitask-based regularization
Figure 13. Various design performance comparison on the manipulation task. Overall MAGIC-optimized top solution candidates perform ~15% better than human-expert design. Different optimization candidates show varying levels of performance at different sub-tasks, allowing a human to make a final decision about the performance trade-offs.

  Finally, we investigate the direction of task-based regularization, through the exposure of the robot to a wider set of manipulation tasks. There exists a large number of regularization approaches applied during the optimization to improve the practicality of the design such as energy, or material penalties. However, we intend to avoid an explicit regularization that can directly impact the final morphology. Instead, we seek implicit regularization via learning on multiple tasks. We repeat data collection, training, and optimization procedures with manipulation tasks and report the performance of multiple solution candidates in Fig.13.

Figure 14. Task Complexity Effects on Robot Design Optimized link lengths of the robot morphologies tend to converge to more plausible solutions when optimized on more complex tasks.

  In addition to seeing similar trends in performance improvements of MAGIC-optimized design $m^\star$ in manipulation similar to reaching task, we also observe overall improvements in the robot design solutions. Fig 14. compares two robot morphologies: one optimized for the reaching task, and one that is optimized for the manipulation task. Not surprisingly, the type and complexity of the tasks being considered could significantly impact the optimal morphology design. Hence, including multiple tasks during the optimization process can lead to a more regularized morphology, with shorter and more manageable links that are likely easier to manufacture.



Read the paper link
Preprint 2023 (arXiv)