Where To Start? Transferring Simple Skills to Complex Environments
Authors: Vitalis Vosylius
Edward Johns
Venue: CoRL 2022 Poster (2022)
Keywords: Robot manipulation
planning
obstacle avoidance

Abstract: Robot learning provides a number of ways to teach robots simple skills, such as grasping. However, these skills are usually trained in open, clutter-free environments, and therefore would likely cause undesirable collisions in more complex, cluttered environments. In this work, we introduce an affordance model based on a graph representation of an environment, which is optimised during deployment to find suitable robot configurations to start a skill from, such that the skill can be executed without any collisions. We demonstrate that our method can generalise a priori acquired skills to previously unseen cluttered and constrained environments, in simulation and in the real world, for both a grasping and a placing task. 

Where To Start? Transferring Simple Skills to Complex Environments
Vitalis Vosylius and Edward Johns
The Robot Learning Lab
Imperial College London
United Kingdom
e.johns)@imperiaa
bbstract:
Robot learning provides a number ways to teach robots simple skills, such grasping. However, these skills are usually trained in open, clutter-free envi- ronments, and therefore would likely cause undesirable collisions in more com- plex, cluttered environments. In this work, we introduce affordance model based on a graph representation ing deployment to find suitable robot configurations to start skill from, such that the skill can be executed without any collisions. We demonstrate that our method generalise priori acquired skills previously unseen cluttered and constrained environments, in simulation and in the real world, for both a grasping and placing task. Videos are available on our project webpage https: https://www.robest robot -erooottteeaannnnggg uk/where- to-taar..--ttrt..
an environment, which is optimised dur-
Keywords: Robot manipulation, planning, obstacle avoidance
Optimisation
on Slariing Contiguration
Step Find Configuration
Starting Configuration
Through Kineratic Planning
Overview of the proposed framework for one of considered tasks placing a held object into a novel container (white bowl). First, the robot observes the scene using head and wrist- mounted cameras. Then, it finds good configuration to start a priori learnt skill, by optimising the learnt heterogeneous graph-based affordance model. From this configuration, the robot should be able complete the task without colliding with obstacles. The starting configuration then reached via kinematic planning, after which the placing skill is executed.
Figure 1:
Introduction
There are many methods for acquiring simple robot manipulation skills, such as grasping, placing insertion. Such skills can be learnt through methods such as imitation learning [1] reinforce-
6th Conference on Robot Learning (CoRI. 2022), Auckland, New Zealand.

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ment learning [2]. However, since these methods are data-driven, they would require training across a huge range of environments if the task is to execute the skills whilst avoiding collisions with the environment. And since this is typically impractical, these skills are typically trained in open, unconstrained environments, without enabling transfer of the skill to more complex, cluttered en- vironments. This then raises the following question: how do we maintain the high performance of a skill that was trained in obstacle-free environments, whilst ensuring that it does not collide with obstacles in new cluttered environments?
In complex environments, reactively avoiding obstacles by adding small control adjustments to a leamed skill might not be enough, resulting in a need to completely alter the skill and re-learn how to complete the task in the new environment. Therefore, in this paper, we propose address this by training a separate model to find the optimal robot configuration from which executing the a priori acquired skill would complete the task, whilst avoiding collisions with the environment. The model we propose is novel heterogeneous graph-based affordance model [3], which allows us to represent the target object, the obstacles, and the robot, all in the same Cartesian space, and thus exploit the underlying structure of the learning problem. During deployment, given initial observation of the environment, we first maximise the predicted affordance score with respect to the robot configuration using gradient-based optimisation, to find the optimal robot configuration from which to start a skill (step 1, Figure 1). The robot can then reach this configuration using motion planning (step 2), and finally execute the skill to complete the task (step 3).
We evaluate our proposed three-step approach on both grasping and a placing task, in challenging, cluttered and constrained environments, both in simulation and in the real world. We compare it with a single-step end-to-end approach, where skills are trained in cluttered environments directly, and we also study alternative ways to find the optimal starting configuration. We also carry out an ablation study to evaluate the benefits of using heterogeneous graph as the input of our affordance model. Overall, our experiments validate our method as an effective way to transfer simple priori skills to complex environments with previously unseen clutter.
2 Related Work
Robotic Manipulation in Cluttered Environments. Methods that address robotic manipulation in environments where collisions with the scene are undesired have primarily focused on the grasping task [4, 5, 6, 7, 8]. Most works try to predict a stable grasping pose for which the end-effector would not collide with the surrounding objects without taking the complete configuration of the robot into account. [9] learns latent plans and when to switch to a pre-trained target-driven policy. These plans can be sampled, scored and decoded into end-effector policy actions that avoid objects on the tabletop and grasps the target. On the other hand, we consider the complete configuration of the robot and reach an optimal starting configuration using kinematic planning.
Optimising Learnt Models Test Time, Recently, many works have focused on optimising learnt models with respect to their inputs at inference to perform various robotic manipulation tasks [10, 11]. [12] learns an energy function from demonstrations that, when minimised inference, can produce actions for a closed-loop policy. In [13], Cartesian robot representation and joint angles are used as an input, letting the model decide the most useful representation. [14, 15] tackles the grasp synthesis problem by optimising the learnt success probability model at inference. [16] has used learnt models as constraints on the key configuration in a nonlinear trajectory optimisation frame- work. We take a similar approach but use learnt affordance model to find robot configuration from which a specific skill is likely to succeed.
Representation Learning for Robot Manipulation. A large amount of work has focused on find- ing helpful robot and scene representations. Semantic keypoints representing objects proved to be beneficial in various manipulation tasks [17, 18, 19]. [20, 21] learns dense object descriptors that map raw input images to pixel-wise object representations. Recently, representing the scene using graphs and utilising graph neural networks has been extensively explored [22, 23, 24]. On the other hand, complex systems with various actors have been successfully modelled using heterogeneous graphs exploiting their underlying structure [25, 26]. Examples include academic graphs [27, 28), recommendation systems [29, 30), and Linkedln economic graphs [31]. In this work, we also try to exploit the problem's 's underlying structure using heterogeneous graphs.

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Method
3.1 Overview
We address robot manipulation in cluttered and constrained environments using the three-step ap- proach shown in Figure (left). We assume access skill (e-g. grasping, placing, pushing) which has already been acquired (e.g. through imitation learning reinforcement learning), and during deployment, we assume a specification of which object execute the skill on (e.g. through a high- level planner). Throughout this paper, we call this object the larget. In experiments we test method on two skills, grasping and placing, which we train in simulation using behavioural cloning. However, our method can work with any priori acquired skill and is agnostic to how that skill was acquired. Therefore, we now use the general notation т T-) to represent function that executes given skill.
Our work aims to position a robot in such a way that, even cluttered and constrained environments, these a priori acquired skills can be successfully executed without colliding with the surrounding environment. We so by learning an affordance model, for each individual skill. Given an initial observation of the scene and robot configuration, this model predicts whether the skill would be successful if started from this configuration. By maximising the predicted affordance score with respect to the robot's 's joint angles during deployment, a starting configuratio for particular skill can be found and reached using motion planning.
Daploying Framework Inforance
Architecture the Affordance Model
Find masemieing the affordence scom using undient-basod optimisation
Obstacle Cloud
Graph Creation
Nourell Vetworkk
Mow the adion using motion (RRT--Comect)
[0.1]
und
Point Clou
skill
Actian
Inputs
Representation
Graph
Figure 2: (Left) Overall control-flow of our framework during deployment. (Right) Architecture used to learn the affordance model using segmented point clouds and robot joint angles (q). Here, the magenta dashed line represents the gradient flow used optimise the robot joint angles by maximising the affordance score.
3.2 Affordance Model
We assume that even in a cluttered environment, there exists distribution of robot configurations from which an a priori learnt skill would be successfully executed, i.e. Y = 1. To find such configurations,
affordance model po( Y 1|Ptarget; Pobes q) for each skill x(-)- Given an initial observation of the target P Ptorget and its surrounding obstacles Pobs, and the robot configuration q, the model predicts the probability of the robot successfully executing the skill from this configuration guratiin without colliding with any obstacle. We use segmented point clouds to represent Ptarget and Pobs- and assume know which policy execute and which object in the scene the target. Note that considering only the pose of the onf-effector in most cases is not sufficient for this problem, and we need to consider the complete robot configuration (i.e. joint angles).
introduce
3.2.1 Structured Graph Representation
To accurately learn pa (.), the model needs to reason about the target, obstacles, robot's configuration, and the relative position of each these. also needs to capture the capabilities skills and any low-level controllers used to execute them. While priori acquired skills can be designed to work

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on different robots, the affordance model is dependent on the specific robot and its kinematics as it needs to reason about the robot's geometry and its movement during skill execution. Instead of compressing the scene observation into a single vector and relying on neural network to interpret the vector correctly, we want to exploit the underlying structure of this problem. We do so by jointly representing the target, obstacles and the robot heterogeneous graph.
Heterogeneous graphs allow to attach relevant information to different types of nodes and define structured relations between them. This allows to systematically capture numerous semantic rela- tionships across multiple types of objects. More formally, a heterogeneous graph can be represented as a tuple g = (V. F), where V is a node set, & is an edge set, and FF is node feature set. It also has the associated node type mapping function Φ V + A and the edge type mapping function db : E - R, IA| + |R] >2. Here A and R are node and edge types, respectively. For more detailed explanation of heterogeneous graphs and graph neural networks we refer the reader to [32, 33, 34).
While the target and the obstacles in the environment can be represented in Cartesian space as point clouds, the robot configuration q inherently lies in different (joint angle) space. The highly non-linear mapping between the two spaces can make capturing the desired distribution extremely difficult. We, therefore, represent the complete robot configuration not as a list of joint angles but rather as a set of positions of predefined key points on the robot that can be calculated using fully differentiable Forward Kinematics. These robot key points can. for example, be the centre of mass for each link of the robot. To differentiate between robot key points in an unordered set, each has a feature vector containing a one-hot encoding to identify it.
Using the entire point clouds in the heterogeneous graph would result in a massive structure hin- dering the learning process. We, therefore, use Pointnet++ set abstraction layers [35] to coarsen the point cloud by sampling a fraction of points and locally encoding their neighbourhood. Together with the robot's key points, we now have three sets points together with their local feature vectors ((post, F.J, {pos., Fo}, Fr representing the target, obstacles and the robot, respectively. We use these create fully connected heterogeneous graph that jointly represents the robot, the target object and the obstacles in the environment (see Figure 2 right). We use edge features to repre- sent the relative transformations between node pairs. This way, the network never sees any absolute position, which allows for an easy generalisation through the task space. These relative transforma- tions are differentiable, which still allows us to take gradients with respect to the absolute positions of the robot key points (Figure 3 c). The gradient of each key point shows in which direction it should be moved to increase the affordance prediction. Using kinematic Jacobian and aggregating gradients from the key points, we can compute the gradient with respect to the robot's joint angles (magenta dashed line in Figure 2) and use it in a gradient based-optimisation framework.
a)
b)
Figure 3: Examples of robot configurations that, when a grasping skill is executed, will lead to (a) collision with the environment or (b) successful task completion. Yellow and red point clouds represent the obstacles and the target, respectively. (c) heterogeneous graph representation used and gradients with respect to the positions of the key points defined on the robot (magenta arrows).
3.2.2 Training
We use graph neural network to learn po(.) from the structured input presented in Section 3.2. I. The whole neural network architecture we use to learn po(.) can be seen in Figure 2 (right) and is trained end-to-end. More details the architecture are presented in the supplementary material.
To train this network, we use a dataset composed of positive and negative samples created by rolling out the learnt skills from the different configurations and recording the outcome. We do these roll- outs in different types of procedurally generated simulated scenes with varying amounts of clutter (see Figure 4). One roll-out creates one sample, consisting of the starting robot configuration gi,

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initial scene observation O, and the outcome yr: Drallouts = {9:, Oi, Ve. H We choose where to start the priori acquired skill random such that the wrist camera pointing toward the object, and the robot is not colliding with the environment. We validate the task's success using privileged information available in simulation and assign a label y to the roll-out accordingly (0 or 1). Any roll- out leading to a collision is treated as a failure and assigned the label O. In our implementation, the initial scene observation O₂ is composed of segmented point clouds (Ptarger and Pots) aggregated from two-depth cameras mounted on the robot's head and wrist (see Figure 1). Using multiple cameras allows for better scene coverage, which can be crucial when estimating affordance scores from initial scene observation. Figure (a and b) shows examples of robot configurations in cluttered environment used as negative and positive samples, respectively. We add noise to all the depth images used to obtain point clouds according to [36] and distort the ground truth segmentation maps from simulation to better mimic a real-world setting.
Having both negative and positive examples, we use Binary Cross-Entropy loss function to train the network as an affordance classifier. Additional details regarding the affordance model and its training are presented in the supplementary material.
3.3 Deployment
The first step of our method during deployment is to find optimal robot configuration from which to start an a priori acquired skill using initial scene observation. We do so by optimising the learnt affordance model with respect to the robot's configuration maximise the probability of partie- ular skill being successful. Optimising the learnt model at inference allows us to include any other task or safety-relevan analytical constraint. We utilise this and add joint limit and additional col- lision avoidance (Goo(()) constraints between each link (l) in set of links comprising the robot (L) and known obstacles (obs;) in the environment (E). This results in a constrained, non-convex optimisation problem (Equation 1) which we solve using a gradient-based IPOPT optimiser [37].
max PY = 1|Ptarget, Pobs, Tk; 9)
s.t. Jmin 292 Amin
Icout (FKi(g), ,bs;) 2
In our implementation, known obstacles (E) include the table and bounding boxes of segmented objects as cuboid obstacles. This is coarse estimation of the environment later used by motion planner. Thus, including gool(() ensures that the solution is not considered to be in a collision by the motion planner, which would make finding collision-fres path this configuration infeasible. In our experiments, solving Equation took on average 3.2 seconds and had to be done only once using the initial scene observation. Once the joint angles from which the low-level skill should start are found, we use an RRT-Connect [38] motion planner to find collision-fis free path to reach that configuration (second step of our framework). Finally, the third step is execute the a priori acquired skill completing the task.
Experiments
4.1 Experimental Setup
We evaluated our method on two manipulation tasks in scenes with varying amounts of clutter: 1) grasping novel objects and 2) placing a held object inside novel containers. For the grasping task, the robot aims to grasp the target object stably. For the placing task, the robot starts with a small object inside its gripper, and the goal is to place it inside target container (c.g. bowl). In both cases, collision with obstacles is considered a failure, making the tasks extremely challenging.
We acquire the skill to solve each of these two tasks respectively using Behaviour Cloning (BC) in simulation only. We use various everyday objects from ShapeNet [39] and expert trajectories created by an oracle planner. The skills output 6D end-effectur velocities and use observations from the wrist-mounted depth camera in terms of the segmented point cloud of the target object. Our BC policy networks consist of PointNet++ [35] followed by an LSTM cell (see Figure 2, bottom left). Our closed-loop skills are trained in a tabletop environment with no obstacles. The supplementary material provides more detailed description of our BC skills and how train them.
5

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In simulation, we procedurally generate six types of randomised environments with inherently dif- ferent structures containing the target object and surrounding obstacles (see Figure 4), using the CoppeliaSim [40] simulator with PyRep [41]. Env / is the easiest with no obstacles and serves as baseline showcasing the abilities of learnt skills in isolation. The following environments keep creasing in complexity. Env 2 includes obstacles (primitive shapes and objects from ShapeNet) sur- rounding the target object. Env 3 and 5 have structured obstacles representing drawers and shelves, while environments and 6 add to complexity of 3 and 5 respectively with additional obstacles.
We create Droilouts for each type of environment individually by rolling out skills from 10 different configurations per randomised scene, totalling 500 samples per type of environment. For a fair com- parison, all evaluated methods use the same observation space (segmented point clouds) processed by PointNet++.. Moreover, we use the same number of training examples to train all the methods and roughly match the number of trainable parameters. Baselines trained end-to-end in cluttered scenes use additional data matching the number of training samples used to acquire our BC skills.
In all environments, we randomise obstacles, their shapes and the overall arrangement. In simu- lation, we evaluate all method on 500 randomised environments of each type, where we randomly choose a target object from 100 objects from ShapeNet [39] that have been seen during training. All evaluation environments are also validated using an oracle planner to ensure there is at least one collision-free way to complete the task. We run all experiments with different random seeds six times and present the mean and standard deviation of the success rate. In the supplementary material, we provide results in the graph form showing the trends more clearly.
Env 1
Env 2
Env
Env 4
Env
Env 6
Complexity
Figure 4: Examples of different types of procedurally generated randomised simulated environ-
ments. Inherently different structures introduce a different complexity in each type of environment.
4.2 Comparison Against Learning a Single End-to-End Policy
Our first set of experiments aims to answer the following question: in what type of environments does our three-step approach lead to the most noticeable performance gains over learning a single end-to-end policy directly in cluttered environments?
To answer this, we compare the performance of our method with two fully end-to-end Behaviour Cloning baselines: BC(eef) and BC(joint). Contrary to the skills described in Section 4.1, these policies are trained using demonstrations from scenes with obstacles and need to learn both to avoid obstacles and to complete the task. Generally, this requires controlling the full configuration of the robot. We therefore concatenate current robot joint angles to every point in the point cloud. BC(eef) operates in 6D end-effector action space, whilst BC(joint) set of experiments, all methods are trained using data from each type of environment separately, and evaluated on unseen configurations of these environment types. The results are shown in Table 1.
a joint angle action space. In this
Fm
Fav 5
Method
Grep
Place
Grasp
Place
Greap
Place
Grasp
Place
Grasp
Place
Grapp
Place
BC (520
2.3
41.3.66
47.4
41.44466
11.044
1.7
3.5 2.1
2.
BC (eim)
156458
7.3
179173
$.430
7.0300
0.00000000
27409
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0.0
0.0
0.00000
Dun
826
910220
19
12
19
$4.
330
15.
18
Table 1: Success rates (mean and standard deviation) of our 3-step method compared to single-step
policies trained end-to-end in constrained environments directly.
BC(joint), which uses joint-angle action space, does not yield good results even in environments without obstacles. This expected due to highly non-linear mapping between the joint angle space and Cartesian space, in which point clouds are represented. BC(eef) performs better but struggles in more cluttered environments. Our method, on the other hand, performs well in all environments, showing only minor performance decrease with increasing cluttering. Therefore, to answer

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initial question, the more complex the environment the more noticeable the performance gains of our method over learning single end-to-end policy.
4.3 Different Ways of Finding Starting Configurations
In this second set of experiments, we aim to answer the following question: is optimising the learnt affordance model a better way to find the policy's starting configuration than alternatives?
The alternatives (or baselines) we test include 1) Naive approach, where a random configuration with the wrist camera pointing the target object is found and reached using a motion planner, 2) a Generative approach, which generates a set of joint angles using a trained CVAE [42], con- ditioned on a segmented point cloud, 3-4) two-stage Behaviour Cloning (BC2(eef) and BC2(join)) approaches using different action spaces, where a closed-loop policy is learnt from demonstrations to reach the closest validated starting configuration and execute the skill. The naive baseline serves to validate the need to learn these starting configurations. The generative baseline allows to val- idate our way of obtaining them (regressing joint angles vs optimising affordance score). Finally, the BC2 baselines aim validate the added benefit of the second step in framework reaching a starting configuration using a motion planner. To better showcase the generalisation across unseen clutter, we train all methods using data from 5 of the 6 environment types and evaluate them on the remaining type. The results can be seen in Table 2.
Boy
Eav
Env
Eav B
Method
Crapp
Flace
Grapp
Place
Gruup
Place
Crasp
Flace
Grapp
Place
Grapp
Face
Naive
1.9
SL
65.
46.7 58
60 4.
73
$
Grnentive
$2.1 20.
55
6.7
2..-1.0
46. 13
23.
$400K9
19
42
BC2tcz))
M..0 133
2.
635239
104119
611
332
652222
46414.
355235
13
BC2 (jinn)
11.5
205
7.
37.0270
5.0
28.0
32.9
10.5
8.2
11
32+19
80=3311
Ours
21
11.
18
7.77
8..4
81.7
99833
745
7..5
Table 2: Success rates (mean and standard deviation) of our 3-step method compared to other rele-
vant multi-step approaches.
As expected, the Naive approach performs well in an uncluttered environment, but unlike our method, its performance significantly decreases with increasing clutter. Other baselines also strug- gle with cluttered environments, especially the ones operating directly in the joint angle space. They often start the skill execution when the wrist camera cannot the target. Together, these results provide evidence of the added value of our method compared to the baselines.
4.4 Ablations
Our third set of experiments investigates how important the structured input representation we use is for the affordance model's performance. To do this, we train two affordance models that take as input simple concatenation of the segmented point cloud representing the environment (obtained using PointNet++) and the robot's s sian key points (PointNet( Cartesian)) respectively. We then use this joint representation to predict the affordance score. Additionally, to validate our claim that it is crucial to consider the full con- figuration of the robot, we train a version of our proposed network that uses only key points on the end-effector to represent the robot's is configuration (Ours (eef only)). We evaluate these methods the same way as in Section 4.3. Table shows the results this set of experiments.
in terms of joint angles (PointNet(joint)) and Carte-
Pointteet(jiint) and PointNet Cartesian) did not perform well, showing that structure input rep- resentation really important. Using only key points representing the robot's end-effector instead of the whole robot, on the other hand, yielded better results in Env 1. However, this approach did not perform well in more cluttered and constrained environments, where is crucial to consider the whole configuration of the robot to avoid collisions and complete the task.
Methad
Graep
Grap
Oreg
Gmap
Farx
Graup
133
onl!
Table 3: Success rates (mean and standard deviation) of our 3-step method using different represen- tations of the seene and the robot to learn the affordance model.
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4.5
Real World Evaluation
Env 1
Env
Crv 3
Grasping
Placing
Figure 5: (Left) Examples of different types of environments used in real-world experiments. (Right) Objects used to evaluate grasping and placing tasks.
Although our method is trained entirely in sim- ulated environments, it uses noisy observations and distorted segmentations and should be able to transfer to the real world. To validate this,
Melhol
Grapp
Place
Grapp
Place
Gruup
Place
Nalve
97
12715
6713
7715
2713
3713
12715
10715
10715
we conduct real-world experiments involving Sawyer robot equipped with a Robotiq F2-85 gripper and head and wrist-mounted Intel Re- alsense D435 depth cameras (see Figure 1). We
Table 4: Success rates our method and the Naive baseline in a real world setting.
evaluate our method in three types of environments with different levels of clutter and five objects unseen during training for both grasping and placing tasks (see Figure 5). We run three evaluations per object for each type of environment, randomising the scene's 's configuration. For segmentation, we use a method for unknown object segmentation [43] and manually specify which object is to be manipulated. The results of the real-world experiments can be seen in Table 4. Our method, trained solely in simulated environments, was able to directly adapt to all types of challenging real- world environments without any major decrease in performance. In comparison, and consistently with the results of our second experiment, the Naive baseline struggled to deal with more complex environments (see Section 4.3).
5 Limitations, Failure Modes and Future Work
Although our method shows promising results in generalising learned skills to unseen cluttered en- vironments, has its limitations. First, for skills trained reality rather than simulation, collecting data for the affordance model could be cumbersome. Second, our learnt affordance model would need be re-trained every time a skill changes. Third, relying on perception pipelines for segmen- tation can lead to failures. Fourth, optimisation of the learnt affordance model is quite sensitive to the initialisation provided and can get stuck in bad local minima. And finally, our method may fail in cases where executing the skill without contact with the environment is impossible, such as with long-horizon skills that require significant movement of the whole robot. However, the ma- jority of manipulation skills acquired using methods such as Imitation Learning or Reinforcement learning are simple and short-horizon. In future work, we plan to explore and address these limita- tions, study the applicability other tasks beyond grasping and placing, and extend approach to onngerhorizon tasks by chaining together multiple skills, aiming towards a full pick-and-place system.
6 Conclusions
In this paper, we proposed a method for the robotic manipulation of objects in cluttered and con- strained scenes, where collision with the environment is undesired. We use an approach that com- bines priori acquired skills with the use of an affordance model of our own design. Optimising this affordance model during deployment allows us to find suitable configurations from which starting a given robotic manipulation skill would lead to its successful completion without collision with the environment. To efficiently learn this affordance model, we introduced a novel, heterogenous graph- based representation that jointly captures information about the target object, scene obstacles, and the robot itself, in a structured way. We showed that our method outperforms various baselines, can generalise to unseen cluttered and constrained scenes, and can transfer from simulation to reality.
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Reviews:
[{"review_id": "l9wooHfv4MV", "reviewer": "Reviewer_rXLB", "summary": "This paper submission proposes a method to adapt *a priori* learned skills in open, obstacle-free environments to newly situated environments that contain clutter.  It is motivated by the fact that robots exist in complex and often cluttered environments, and the key hypothesis is that *adapting* a previously acquired skill to newly encountered clutter can prove more sample-efficient than learning from scratch in multiple, diverse cluttered environments.  The proposed method uses a graph-based affordance model with heterogeneous nodes, towards exploiting the underlying structure of the manipulation problem.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "This paper addresses an important and relevant open problem in Robotics for the types of complex environments in which robots exist:  adapting object-centric control skills to execution in cluttered environments.  The method proposed is based upon an interesting and intuitive idea, leveraging a graph-based representation to exploit the inherent spatial relationships that exist between the robot, the target object (to be manipulated), and nearby obstacles present.  My primary questions are with regard to the evaluation.\n\nOverall strengths and suggestions for improvement are summarised below.\n\nMore detailed strengths of paper:\n+  The paper uses a principled and concise formalisation of the manipulation problem.  The representation captures important expressivity by including heterogeneous node and edge types and expected spatial structure that can be exploited by using an underlying graph representation.\n+  Results in Table 2 show notable outperformance of the other tested methods in Environments 2 and up (where greater environment IDs map to increasingly complex environments).  The ablation experiments (section 4.4) add value, in characterising to some extent how the input representation impacts method efficacy.\n+  There is some evaluation conducted on real robot hardware.\n+  The paper is very well written.  The posing of the research questions to be investigated for each experiment was particularly useful in framing why that experiment was selected and how to interpret the results.\n\nI would suggest the following to improve the paper submission:\n-  I have questions around whether the set of baselines evaluated is sufficient. While some standard imitation learning methods (e.g Behavioural Cloning) are used for comparison, I would like to understand (and thus recommend explanation/discussion around) if there are more relevant SOA imitation learning or reinforcement learning baselines that actually aim to do constrained optimization or manipulation in cluttered/constrained environments?  If there are, why were those not tested as part of the Evaluation?\n-  In Section 4.5 (Table 4), why not use all the same baselines as in Table 2?  For that matter, the same question applies for the results in Table 1.  In Table 4, what actually is the naive baseline and why is it the only one included?  Looking at Table 2, the naive baseline often does not outperform the other baselines.  BC seems to generally perform better; either way, results for BC are reported in the other experiments (Tables 1 and 2), so I would expect it to be included in the real world evaluation.  These questions around the baselines are important because this speaks to how useful the results and insights from the Evaluation are towards assessing the efficacy of the method and the value it adds to the set of existing/SOA approaches.\n\nAdditional minor comments:\nIn section 3.2.2 (page 5, line 139 and Equation 1), what is q?  Perhaps it is the robot starting configuration (as implied in line 138); however, I would recommend explicitly defining q, O, and y. \nIn Tables 1 and 2, would suggest **bolding** the best performing method(s) for each column.  This makes it easier to understand how the proposed method ranks comparably across tasks and environments.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "I would recommend accepting this paper, based upon its contribution of a method for adapting acquired skills to new cluttered (and thus more complex environments).  To my knowledge, this is an important and still open problem in Robot Manipulation.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Additional details required", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "issues": "See limitation listed above.", "reviewer_expertise": "3: The reviewer is fairly confident that the evaluation is correct"}, "extra_text": {}}, {"review_id": "gHYjKcqBAZg", "reviewer": "Reviewer_oZZf", "summary": "The authors proposed a graph neural network model to determine the initial robot configuration in a cluttered environment. This initial configuration is then deployed into a motion planning model to solve the collision-free manipulation task. The authors show in both simulations and in the real world how this model can help improve the success rate of skill transfer.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The document is generally well written and easy to understand.\n\nThe numbers clearly illustrate the proposed methodology.\n\nThe number of random seeds used for statistical analysis is insufficient.\n\nI have some doubts about the scalability and generalizability of the proposed approach. The proposed model is trained in a specific scenario with limited deviations. The proposed approach has also been validated in a similar setting. In addition, the affordance model is also conditioned on pre-trained skills. All these settings limit applicability to a broader range of tasks and environments.\n\nThis paper lacks rational motivation. The paper's objective is to address the problem of collision avoidance for manipulation tasks. Many different methods already exist to deal with unstructured environments, such as the primitive approximations [1] or the SDF-based approaches [2, 3], so why is the proposed method beneficial compared to these approaches? Also, different approaches exist for skill transfer, such as policy composition [4], and learning residual policies [5]. What is the advantage of only finding the initial configuration?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "As stated in the strength and weaknesses, I think the paper does not have a clear motivation. \nThe scalability and generalizability are not clear to me. I would appreciate it if the author could conduct more experiments on it. \nThe advantage and drawbacks are not well discussed. The experiment runs only 3 seeds which are not statistically sufficient. \nI would suggest rejecting the paper in its current status. \n", "technical_quality": "Fair: Significant additional evidence required to support the claims.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Additional details required", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"}, "extra_text": {"issues": "1. The proposed method tries to learn an initial configuration to simplify the subsequent manipulation task. However, reaching this configuration could already be problematic, especially when the environment has a very narrow feasible space. When a simple motion planner is used to reach the initial configuration as described by the authors, why not use a motion planner to solve the whole problem?\n2. How the downsampling of the point cloud could encode the geometry of the environment? What is the local feature vector? Why it’s necessary to learn the affordance model? It seems to me that the model only needs a subset of the point cloud to compute the transformation.\n3. What is $y_i$ in section 3.2.2?\n4. In the deployment, the author also added a collision avoidance constraint. Why is this constraint needed? Shouldn’t the model learn it?\n5. I think the joint limit constraint could be parameterized differently, such as a sigmoid function [1]. In such a way, you could avoid the constraints in the optimization.\n6. It would be more beneficial to compare with some proper baselines such as [1], [5]. \n\n[1] Ratliff, N.D., et al, Riemannian motion policies. arXiv preprint arXiv:1801.02854 (2018)\n\n[2] Finean, et al. \"Predicted composite signed-distance fields for real-time motion planning in dynamic environments.\" *Proceedings of the International Conference on Automated Planning and Scheduling*. Vol. 31. 2021.\n\n[3] Liu, Puze, et al. \"Regularized Deep Signed Distance Fields for Reactive Motion Generation.\" *arXiv preprint arXiv:2203.04739* (2022).\n\n[4] Todorov, Emanuel. \"Compositionality of optimal control laws.\" *Advances in neural information processing systems* 22 (2009).\n\n[5] Johannink, Tobias, et al. \"Residual reinforcement learning for robot control.\" *2019 International Conference on Robotics and Automation (ICRA)*. IEEE, 2019."}}, {"review_id": "cQzp4IezW8_", "reviewer": "Reviewer_dTVn", "summary": "The paper proposed a novel heterogeneous graph-based affordance model to find the optimal robot configuration for executing the a priori acquired skill would complete the task, whilst avoiding collisions with the environment. The main contribution of the paper is an three-step approach that allows the trained policies trained in obstacle-free environments to be excuted in new cluttered environments wihtout any \ncollisions. They demonstrate that the proposed method can achieve good performance of the tasks on different tasks in challenging, cluttered and constrained environments.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The idea of 3 steps task deployments that covers finding the starting configuration, move to the start configuration, execute the skill is interesting.\n\nThe idea of learning starting configuration with GNN is interesting.\n\nThe experiment result shows interesting behaviors learned by the proposed method. The comparison to prior methods in general supports the idea in this work.\n\nIt’s not clear if the proposed method can handle tasks with the environment that, the a prior learnt skill would not be successfully executed. ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "The paper presents a concrete learning algorithm and demonstrates good performance on a suite of difficult problems. However, the exposition of the paper could be improved and more discussions/comparisons would make the paper even stronger.", "technical_quality": "Good: Generally convincing, but some additional experiments or analyses needed.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "reviewer_expertise": "3: The reviewer is fairly confident that the evaluation is correct"}, "extra_text": {"issues": "The result in the video shows that the method can learn to solve a particular task with two steps. Are those environement always  meet the assumption that  there exists a distribution of robot configurations from which an a priori learnt skill would be successfully executed?\n\nIt is confusing that, if a priori acquired skills are trained in a enviroment without constraints. Is there any cases that, after finding the starting configuration, the robot still can not successfully complete the task? As the a priori acquired skills can not avoiding the collision. \n\nAlso, how would the method work if a priori learnt skill would not be successfully executed?\n\nAdd discussion regarding if the reason that the proposed method outperform the end-to-end algorithm is because the a prior skill reduce the potential search area?\n"}}, {"review_id": "SchbBlZzQ1t", "reviewer": "Reviewer_jW3a", "summary": "The paper presents a approach that can provide suitable starting poses for a priori known skills.\nSkills in the paper are for grasping and placing tasks, but there is not reason apparent why it should \nnot generalize to other skills. The approach can facilitate robot manipulation in cluttered environments\nThe learned model can be optimized at runtime to adapt to current environments. The authors propose the \nheterogeneous graph-based representation. \nEvaluation is done in simulation and on a real robot to both show the need of the component and its performance\ncompared to alternative approaches.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The paper describes the approach quite nicely and is easy to follow. Approach explanation and evaluation is \nnicely done. The performance of the approach is very promising and seems to generalize quite well.\n\nThere is no mentioning of any time requirements for training, learning, or optimization at test time. This information \nwould be of great interest and a crucial detail to judge the performance and usability of the approach.\n\nThe limitations section mentions a few possible reasons for failure cases. A more differentiated discussion would have been \nhelpful. How often did a failure get caused by what problem? The failure rate is quite high even in simulation that \nan actual discussion would be useful. Also a discussion on how/what could remedy these issues is missed and what \nside-effects that would cause (e.g., increase input complexity at cost of more learning data needed).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "The paper is easy to read, lays out the proposed approach clearly, and convincingly shows the performance with several experiments.\nA few details were missing, but it is minor. I found the paper very informative and would like to see a continuation of this research line. \nThe approach also promises to be very useful for real robot manipulation. ", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Excellent: Enjoyable to read.", "potential_impact": "", "robotics_focus": "Sufficient demonstration on hardware", "quality_of_limitations": "Additional details required", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "reviewer_expertise": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "extra_text": {"issues": "- There is no mentioning of any time requirements for training, learning, or optimization at test time. This information \nwould be of great interest and a crucial detail to judge the performance and usability of the approach.\n\n- A bit extended discussion and details in the limitations section would improve the paper.\n\n- I might have overlooked it, but it would be nice to explicitely state that the learned model is dependent for a specific robot and \nhas to be relearned for different robot kinematics."}}]

Author rebuttal:

Decision: Accept (poster)