Year: 2021

Abstract:
Multiple domains like vision, natural language, and audio are witnessing tremen1 dous progress by leveraging Transformers for large scale pre-training followed by 2 task specific fine tuning. In contrast, in robotics we primarily train a single robot 3 for a single task. However, modular robot systems now allow for the flexible com4 bination of general-purpose building blocks into task optimized morphologies. 5 However, given the exponentially large number of possible robot morphologies, 6 training a controller for each new design is impractical. In this work, we pro7 pose MetaMorph, a Transformer based approach to learn a universal controller 8 over a modular robot design space. MetaMorph is based on the insight that robot 9 morphology is just another modality on which we can condition the output of a 10 Transformer. Through extensive experiments we demonstrate that large scale pre11 training on a variety of robot morphologies results in policies with combinato12 rial generalization capabilities, including zero shot generalization to unseen robot 13 morphologies. We further demonstrate that our pre-trained policy can be used for 14 sample-efficient transfer to completely new robot morphologies and tasks. 15

Multiple domains like vision, natural language, and audio are witnessing tremen-1 dous progress by leveraging Transformers for large scale pre-training followed by2 task specific fine tuning. In contrast, in robotics we primarily train a single robot3 for a single task. However, modular robot systems now allow for the flexible com-4 bination of general-purpose building blocks into task optimized morphologies.5 However, given the exponentially large number of possible robot morphologies,6 training a controller for each new design is impractical. In this work, we pro-7 pose MetaMorph, a Transformer based approach to learn a universal controller8 over a modular robot design space. MetaMorph is based on the insight that robot9 morphology is just another modality on which we can condition the output of a10 Transformer. Through extensive experiments we demonstrate that large scale pre-11 training on a variety of robot morphologies results in policies with combinato-12 rial generalization capabilities, including zero shot generalization to unseen robot13 morphologies. We further demonstrate that our pre-trained policy can be used for14 sample-efficient transfer to completely new robot morphologies and tasks.15
1 INTRODUCTION16
The field of embodied intelligence posits that intelligent behaviours can be rapidly learned by agents17 whose morphologies are well adapted to their environment (Brooks, 1991; Pfeifer & Scheier, 2001;18 Bongard, 2014; Gupta et al., 2021). Based on this insight, a robot designer is faced with a predica-19 ment: should the robot design be task specific or general? However, the sample inefficiency of20 tabula rasa deep reinforcement learning and the challenge of designing a single robot which can21 perform a wide variety of tasks has led to the current dominant paradigm of ‘one robot one task’. In22 stark contrast, domains like vision (Girshick et al., 2014; He et al., 2020) and language (Dai & Le,23 2015; Radford et al., 2018), which are not plagued by the challenges of physical embodiment, have24 witnessed tremendous progress especially by leveraging large scale pre-training followed by trans-25 fer learning to many tasks through limited task-specific fine-tuning. Moreover, multiple domains26 are witnessing a confluence, with domain specific architectures being replaced by Transformers27 (Vaswani et al., 2017), a general-purpose architecture with no domain-specific inductive biases.28
How can we bring to bear the advances in large-scale pre-training, transfer learning and general-29 purpose Transformer architectures, to the field of robotics? We believe that modular robot systems30 provide a natural opportunity by affording the flexibility of combining a small set of general-purpose31 building blocks into a task-optimized morphology. Indeed, modularity at the level of hardware is a32 motif which is extensively utilized by evolution in biological systems (Hartwell et al., 1999; Kashtan33 & Alon, 2005) and by humans in many modern engineered systems. However, prior works (Wang34 et al., 2018; Chen et al., 2018; Sanchez-Gonzalez et al., 2018) on learning policies that can generalize35 across different robot morphologies have been limited to: (1) manually constructed variations of a36 single or few base morphologies, i.e. little diversity in the kinematic structure; (2) low complexity37 of control (≤ 7 degrees of freedom); (3) using Graph Neural Networks (Scarselli et al., 2008) based38 on the assumption that kinematic structure of the robot is the correct inductive bias.39
In this work, we take a step towards a more challenging setting (Fig. 1) of learning a universal40 controller for a modular robot design space which has the following properties: (a) generalization41 to unseen variations in dynamics (e.g. joint damping, armature, module mass) and kinematics (e.g.42 degree of freedom, morphology, module shape parameters) and (b) sample-efficient transfer to new43
morphologies and tasks. We instantiate the exploration of this general setting in the UNIMAL design44 space introduced by Gupta et al. (2021). We choose the UNIMAL design space as it contains a45 challenging (15−20 DoFs) distribution of robots that can learn locomotion and mobile manipulation46 in complex stochastic environments. Learning a single universal controller for a huge variety of47 robot morphologies is difficult due to: (1) differences in action space, sensory input, morphology,48 dynamics, etc. (2) given a modular design space, not all robots are equally adept at learning a task,49 e.g. some robots might inherently be less sample-efficient (Gupta et al., 2021).50
To this end, we propose MetaMorph, a method to learn a universal controller for a modular robot51 design space. MetaMorph is based on the insight that robot morphology is just another modal-52 ity on which we can condition the output of a Transformer. MetaMorph tackles the challenge of53 differences in embodiment by leveraging a Transformer based architecture which takes as input a54 sequence of tokens corresponding to the number of modules in the robot. Each input token is created55 by combining proprioceptive and morphology information at the level of constituent modules. The56 combination of proprioceptive and embodiment modalities and large scale joint pre-training leads57 to policies which exhibit zero-shot generalization to unseen variations in dynamics and kinemat-58 ics parameters and sample-efficient transfer to new morphologies and tasks. Finally, to tackle the59 differences in learning speeds of different robots, we propose dynamic replay buffer balancing to60 dynamically balance the amount of experience collection for a robot based on its performance.61
In sum, our key contributions are: (1) we introduce MetaMorph to learn a universal controller for a62 modular design space consisting of robots with high control complexity for challenging 3D locomo-63 tion tasks in stochastic environments; (2) we showcase that our learned policy is able to zero-shot64 generalize to unseen variations in dynamics, kinematics, new morphologies and tasks, which is par-65 ticularly useful in real-world settings where controllers need to be robust to hardware failures; (3)66 we analyze the learned attention mask and discover the emergence of motor synergies (Bernstein,67 1966), which partially explains how MetaMorph is able to control a large number of robots.68
2 RELATED WORK69
Prior works on learning control policies which can generalize across robot morphologies have pri-70 marily focused on parametric variations of a single (Chen et al., 2018) or few (2 − 3) robot types71 (Wang et al., 2018; Sanchez-Gonzalez et al., 2018; Huang et al., 2020; Kurin et al., 2021). For gener-72 alizing across parametric variations of a single morphology, various approaches have been proposed73 like using a learned hardware embedding (Chen et al., 2018), meta-learning for policy adaptation74 (Al-Shedivat et al., 2017; Ghadirzadeh et al., 2021), kinematics randomization (Exarchos et al.,75 2020), and dynamics randomization (Peng et al., 2018). In case of multiple different morpholo-76 gies, one approach to tackle the challenge of differences in action and state spaces is to leverage77 Graph Neural Networks (Scarselli et al., 2008; Kipf & Welling, 2017; Battaglia et al., 2018). Wang78 et al. (2018); Huang et al. (2020) use GNNs to learn joint controllers for planar agents (≤ 7 DoFs).79 Blake et al. (2021) propose freezing selected parts of networks to enable training GNNs for a single80 morphology but with higher control complexity. The usage of GNNs is based on the assumption81 that the robot morphology is a good inductive bias to incorporate into neural controllers, which can82 be naturally modelled by GNNs. Recently, Kurin et al. (2021) also proposed using Transformers83 for training planar agents. Our work differs from Kurin et al. (2021) in the diversity and scale of84 training robots, complexity of the environments, conditioning the Transformer on morphological85 information, and showcasing strong generalization to unseen morphologies and tasks (see § B.1).86
Another closely related line of work is the design of modular robot design spaces and developing al-87 gorithms for co-optimizing morphology and control (Sims, 1994) within a design space to find task-88 optimized combinations of controller and robot morphology. When the control complexity is low,89 evolutionary strategies have been successfully applied to find diverse morphologies in expressive90 soft robot design spaces (Cheney et al., 2014; 2018). In the case of rigid bodies, Ha (2019); Schaff91 et al. (2019); Liao et al. (2019) have proposed using RL for finding optimal module parameters of92 fixed hand-designed morphology for rigid body robots. For more expressive design spaces, GNNs93 have been leveraged to share controller parameters (Wang et al., 2019) across generations or develop94 novel heuristic search methods for efficient exploration of the design space (Zhao et al., 2020). In95 contrast to task specific morphology optimization, III et al. (2021) propose evolving morphologies96 without any task or reward specification. Finally, for self reconfigurable modular robots (Fukuda &97 Nakagawa, 1988; Yim et al., 2007), modular control has been utilized in both real (Rubenstein et al.,98 2014; Mathews et al., 2017) and simulated (Pathak et al., 2019) systems.99
3 LEARNING A UNIVERSAL CONTROLLER100
We begin by reviewing the UNIMAL design space and formulating the problem of learning a uni-101 versal controller for a modular robot design space as a multi-task reinforcement learning problem.102
3.1 THE UNIMAL DESIGN SPACE103
An agent morphology can be naturally represented as a kinematic tree, or a directed acyclic graph,104 corresponding to a hierarchy of articulated 3D rigid parts connected via motor actuated hinge joints.105 The graph G := (V, E) consists of vertices V = {v1, ..., vn} corresponding to modules of the design106 space, and edges eij ∈ E corresponding to joints between vi and vj . Concretely, in the UNIMAL107 (Gupta et al., 2021) design space, each node represents a component which can be one of two types:108 (1) a sphere parameterized by radius and density to represent the head of the agent and form the root109 of the tree; (2) cylinders parameterized by length, radius, and density to represent the limbs of the110 robot. Two nodes of the graph can be connected via at most two motor-actuated hinge joints (i.e. G111 is a multi-graph), parameterized by joint axis, joint limits and a motor gear ratio.112
3.2 JOINT POLICY OPTIMIZATION113
The problem of learning a universal controller for a set of K robots drawn from a modular robot de-114 sign space is a multi-task RL problem. Specifically, the control problem for each robot is an infinite-115 horizon discounted Markov decision process (MDP) represented by a tuple (S,A, T,R,H, γ),116 where S represents the set of states, A represents the set of available actions, T (st+1|st, at) rep-117 resents the transition dynamics, R(s, a) is a reward function, H is the horizon and γ is the discount118 factor. At each time step, the robot k receives an observation skt , takes an action a k t , and is given a119
reward rkt . A policy πθ(a k t |skt ) models the conditional distribution over action akt ∈ A given state120 skt ∈ S. The goal is to find policy parameters θ which maximize the average expected return across121 all tasks: R = 1K ∑K k=0 ∑H t=0 γ
trkt . We use Proximal Policy Optimization (PPO) (Schulman et al.,122 2017), a popular policy gradient (Williams, 1992) method for optimizing this objective.123
4 METAMORPH124
Progress in model-free reinforcement learning algorithms has made it possible to train locomotion125 policies for complex high-dimensional agents from scratch, albeit with tedious hyperparamter tun-126 ing. However, this approach is not suitable for modular design spaces containing exponentially many127 robot morphologies. Indeed, Gupta et al. (2021) estimates that the UNIMAL design space contains128 more than 1018 robots. Hence, learning a separate policy for each robot is infeasible. However, the129 modular nature of the design space implies that while each robot morphology is unique, it is still130 constructed from the same set of modules and potentially shares subgraphs of the kinematic tree131 with other morphologies. We describe how MetaMorph exploits this structure to meet the challenge132 of learning a universal controller for different morphologies.133
4.1 FUSING PROPRIOCEPTIVE STATES AND MORPHOLOGY REPRESENTATIONS134
To learn policies that can generalize across morphologies, we must encode not only proprioceptive135 states essential for controlling a single robot, but also morphological information. From a multi-task136 RL perspective, this information can be viewed as a task identifier, where each task corresponds to137 a different robot morphology, all drawn from the same modular design space. Hence, instead of138 learning a policy which is agnostic to the robot morphology, we need to learn a policy conditioned139 on the robot morphology. Consequently, at each time step t (we drop the time subscript for brevity),140 the robot receives an observation sk = (skm, s k p, s k g) which is composed of the morphology repre-141
sentation (skm), the proprioceptive states (s k p), and additional global sensory information (s k g ). See142 § A.1 for a detailed description of each observation type.143
4.2 MORPHOLOGY AWARE TRANSFORMER144
The robot chooses its action via a stochastic policy πθ(akt |skt ) where θ are the parameters of a pair145 of deep neural networks: a policy network that produces an action distribution (Fig. 2), and a critic146 network that predicts discounted future returns. We use Transformers (Vaswani et al., 2017) to147 parametrize both policy and critic networks as described in detail below.148
Encode. We make a distinction between how we process local and global state information. Concretely, let sk = (skl , s k g) where s k l = (s k m, s k p). Since the number of joints between two modules can vary, we zero pad skli to ensure that input observation vectors are of the same size, i.e. s k l ∈ RN×M . In order to provide an arbitrary robot morphology as input to the Transformer, we first create a 1D sequence of local observation vectors by traversing the kinematic tree in depth first order starting at the root node (torso in case of the UNIMAL design space). We then apply a single layer MLP independently to skli to create a D dimensional module embedding. We also add learnable 1D position embeddings to the module embeddings to automatically learn positional information:
m0 = [φ(s k l1 ;We); · · · ; φ(s k lN ;We)] + Wpos, (1)
where φ(·) is the embedding function, We ∈ RM×D are the embedding weights, and Wpos ∈149 RN×D are the learned positional embeddings. Note that in practice we zero pad the input sequence150 of local observation vectors for efficient batch processing of multiple morphologies.151
Process. From the module embeddings described above, we obtain the output feature vectors as:
m′` = MSA(LN(m`−1)) + m`−1, ` = 1 . . . L (2)
m` = MLP(LN(m ′ `)) + m ′ `, ` = 1 . . . L (3)
where MSA is multi-head attention (Vaswani et al., 2017) and LN is Layernorm (Lei Ba et al., 2016).152
Decode. We integrate the global state information skg consisting of high-dimensional sensory input from camera or depth sensors. Naively concatenating skg and s k l in the encoder has two downsides: (1) it dilutes the importance of low-dimensional local sensory and morphological information; (2)
it increases the number of Transformer parameters due to an increase in the dimensionality of the input embedding (D). Instead, we obtain the outputs of policy network as follows:
g = γ(skg ;Wg), µ(s k) = φ(mli ,g;Wd), πθ(a k|sk) = N ({µ(ski )}Ni ,Σ), (4)
where γ(·) is a 2-layer MLP with parameters Wg , φ(·) is an embedding function with Wd as the153 embedding weights. The action distribution is modeled as a Gaussian distribution with a state-154 dependent mean µ(ski ) and a fixed diagonal covariance matrix Σ. Similarly, for the critic network,155 we estimate value for the whole morphology by averaging the value per limb.156
4.3 DYNAMIC REPLAY BUFFER BALANCING157
Joint training of diverse morphologies is challenging as different morphologies are adept at per-158 forming different tasks. Consequently, some morphologies might be inherently better suited for the159 pre-training task. Let us consider two robots: (A) Robot A locomotes in a falling forward manner,160 i.e., robot A is not passively stable; (B) Robot B is passively stable. Especially with early termina-161 tion, robot A will keep falling during the initial phases of training, which results in shorter episode162 lengths, whereas robot B will have longer episode lengths. Hence, more data will be collected for163 robot B in the earlier phases of training, and in turn will lead to robot B learning even faster, thereby164 resulting in a ‘rich gets richer’ training dynamic. However, our goal is to ensure that all morpholo-165 gies have a similar level of performance at the end of training as we want to generalize across the166 entire distribution of morphologies.167
To address this issue, we propose a simple dynamic replay buffer balancing scheme. On-policy algorithms like PPO (Schulman et al., 2017) proceed in iterations that alternate between experience collection and policy parameter update. Let Ek be any performance metric of choice, e.g. normalized reward, episode length, success ratio, etc. Let τ be the training iteration number. At each iteration, we sample the kth robot with probability Pk, given by:
Pk = Eβk∑ Eβi
Eτk = αEτk + (1− α)E (τ−1) k , (5)
where α ∈ [0, 1] is the discount factor and the exponent β determines the degree of dynamic pri-168 oritization, with β = 0 corresponding to the uniform case. In practice, we use episode length as169 our performance metric. We determine Pk by replacing Ei with 1000Ei , where the numerator is the170 maximum episode length.171
5 EXPERIMENTS172
In this section, we evaluate our method MetaMorph in different environments, perform extensive ab-173 lation studies of different design choices, test zero-shot generalization to variations in dynamics and174 kinematics parameters, and demonstrate sample efficient transfer to new morphologies and tasks.175 For qualitative results, please refer to the video on our project website 1.176
1https://metamorph-iclr.github.io/site/
5.1 EXPERIMENTAL SETUP177
We create a training set of 100 robots from the UNIMAL design space (Gupta et al., 2021) (see178 § A.2). We evaluate MetaMorph on three different environments (Fig. 3) using the MuJoCo sim-179 ulator (Todorov et al., 2012). In all 3 environments the goal of the agent is to maximize forward180 displacement over the course of an episode which lasts 1000 timesteps. The 3 environments are: (1)181 Flat terrain (FT); (2) Variable terrain (VT): VT is an extremely challenging environment as during182 each episode a new terrain is generated by randomly sampling a sequence of terrain types and inter-183 leaving them with flat terrain. We consider 3 types of terrains: hills, steps, and rubble; (3) Obstacles:184 cuboid shaped obstacles of varying sizes on flat terrain.185
We use a dense morphology independent reward function for all our tasks as it is not feasible to186 design a reward function tailored to each morphology. In all tasks, our reward function promotes187 forward movement using small joint torques (the latter obtained via a small energy usage penalty).188 In addition, as described in §4.3, we use early termination across all environments when we detect a189 fall (i.e. if the torso height drops below 50% (FT, Obstacles) or 30% (VT) of its initial height).190
5.2 BASELINES AND ABLATIONS191
Baselines: We compare against the following baselines:192
(1) GNN: We modify the NerveNet model proposed by193 Wang et al. (2018) to learn control policies for complex194 3D morphologies with variable number of joints. Specif-195 ically, we replace how the NerveNet model receives in-196 put and produces output by our encode and decode steps197 respectively (§4.2). In addition, the model receives the198 same observation as MetaMorph i.e. sk = (skm, s k p, s k g)199 and is trained with dynamic replay buffer sampling. Thus,200 the only difference is in the process step. This helps test if201 the domain-specific inductive bias of the robot kinematic202 tree in the GNN is necessary.203
(2) MLP: We train all 100 robots separately with a 2-layer204 MLP and report the average performance. This baseline205 serves as an approximate upper bound for our method.206
Ablations: We also do an ablation study of different de-207 sign choices involved in our method. We refer our full208 method as MetaMorph and consider the following abla-209
tions: (1) MetaMorph-NPE: no learned position embeddings; (2) MetaMorph-NM: we only provide210
sk = (skp, s k g) as inputs, i.e. the model does not have access to information about the robot morphol-211 ogy.212
Fig. 4 shows learning curves across 3 environments for training 100 morphologies. In all environ-213 ments MetaMorph can successfully match the average reward achieved via the per morphology MLP214 baseline on both FT, and obstacle environment. While MetaMorph performance in VT is slightly215 below the MLP baseline, we note that it has not saturated and we stopped training at 108 iterations216 across all three environments. Moreover, MetaMorph is significantly more sample-efficient (5×)217 than training independent MLPs (5 × 106 iterations per robot). The GNN baseline saturates at a218 level 2 to 3 times below MetaMorph. In GNN based models, locality and neighborhood connectiv-219 ity is explicitly baked into the model. Interestingly, just like ViT (Dosovitskiy et al., 2021) sparingly220 utilizes the 2D neighborhood structure of images at the beginning of the model by cutting the image221 into patches, MetaMorph uses the graph structure of the robot in the beginning by creating a 1D222 sequence corresponding to the kinematic tree by traversing the graph in depth first order. More-223 over, the position embeddings carry no information about the graph structure and are learned from224 scratch. We highlight that the learned position embeddings significantly improve the performance225 of MetaMorph, just as they do in Transformer based image classification. Finally, without access226 to the morphological information, MetaMorph-NM fails to learn a policy that can control diverse227 robot morphologies. All of this substantiates our central claim that morphological state information228 is necessary to learn successful control policies, although the kinematic graph need not be explicitly229 baked into neural architectures to learn policies capable of controlling diverse robot morphologies.230 Finally, we test the importance of dynamic replay buffer balancing in Fig. 5, and find that balancing231 is necessary to learn a good control policy in 10− 15% of robots across all 3 environments.232
5.3 ZERO-SHOT GENERALIZATION233
Our focus in this work is to learn policies that can generalize to unseen robots drawn from a modular234 robot design space. In this section, we demonstrate that MetaMorph shows favorable generalization235 properties across many different kinematic and dynamic variations.236
Experimental Setup. For each of the 100 training robots, we create a dedicated test set to test zero-237 shot transfer performance across two types of variations: dynamics (armature, density, damping, and238 motor gear) and kinematics (module shape parameters like radius, and length of cylindrical limbs,239 and joint angles). For each training robot, we randomly create 4 different variants for each property,240 i.e. 400 robots with armature variations, and so on. While creating a new variant, we change the241
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Figure 8: Fine tuning: New robot morphologies and tasks. Left: Test environments. Right: Comparison of reward progression of 100 test robots averaged over 3 runs for pre-trained MetaMorph (VT→ Escape, Obstacles→ Obstacles (cylinders)) vs from scratch. Shaded regions denotes standard deviation. Across all environments pre-training leads to strong zero-shot performance and 2− 3× savings in training iterations to achieve the same level of average reward.
relevant property of all modules or joints. See Table 2 for sampling ranges. We then compare242 zero-shot performance averaged over 10 trials.243
Generalization: Dynamics. First, we consider generalization to different dynamics (Fig. 6). We244 find consistently that MetaMorph performs significantly better than MetaMorph-NM and GNN245 across all types of dynamic variations and all environments. In fact, the difference is more pro-246 nounced for harder tasks like VT and Obstacles. We note that this result is surprising as we do not247 do dynamics randomization during training, i.e., all robots in the training set have the same armature248 and damping parameters. Despite this, we see strong generalization performance.249
Generalization: Kinematics. Next, we consider generalization to different kinematics parameters250 (Fig. 6). This is a significantly more challenging setting as the model has to generalize to unseen251 variations in module shape parameters and changes to joint angle ranges. In fact, changes to joint252 angles can significantly alter the range of possible motion and might necessitate a different gait253 for successful locomotion. Consequently, we find that even though MetaMorph exhibits strong254 generalization performance compared to MetaMorph-NM and GNN in all 3 environments, there255 is indeed a performance drop for the challenging setting of variations in joint angles. However, the256 zero-shot performance is encouraging and motivates our next set of experiments on transfer learning.257
258
5.4 SAMPLE EFFICIENT TRANSFER LEARNING259
Experimental Setup. Here we create 100 robots from the UNIMAL design space which were260 not part of the training set, and we demonstrate that MetaMorph shows favorable sample efficient261 transfer to unseen morphologies, and even unseen morphologies performing novel tasks.262
Different morphologies. We first consider sample efficient learning of controllers for new mor-263 phologies on the same task by taking a pre-trained MetaMorph model on an environment, and then264 fine tuning it to learn to control morphologies in the test set. In Fig. 7 we compare the number of265 training iterations required to achieve a particular performance level when we fine tune MetaMorph266
vs training from scratch on the test set. Across all 3 environments we not only find strong zero-shot267 performance , but also 2 to 3 times higher sample efficiency compared to training from scratch.268
Different morphologies and novel tasks. Finally, we consider the most realistic and general setting269 mimicking the promise of modular robots, where we are faced with a novel task and want to use a270 new robot morphology which may be suited for this task. We consider the same set of 100 test271 robots on two new tasks (Fig. 8): (1) Escape: The agent starts at the center of a bowl shaped terrain272 surrounded by small hills (bumps), and has to maximize the geodesic distance from the start location273 (escape the hilly region). (2) Obstacles (cylinders): cylinder shaped obstacles of varying sizes (the274 size distribution is also different from the train task i.e. cuboid shapes). We transfer the learned275 policy from VT and Obstacles to Escape and Obstacles-Cylinders respectively. Again, we find that276 there is a strong zero-shot performance across all 3 environments and fine-tuning is 2 to 3 times277 more sample efficient than training from scratch.278
5.5 EMERGENT MOTOR SYNERGIES279
We next search for a potential explanation for how MetaMorph can coordinate the large number of280 DoFs (∼ 16 × 100) across several agents. Our hypothesis is inspired by Bernstein (1966), which281 proposed the existence of muscle synergies as a neural strategy to simplify the control of multiple282 DoFs by the central nervous system. A muscle synergy corresponds to the constrained movements of283 sets of joints (or muscles) through co-activation by a single neural signal. Such synergies obviate the284 need to control all joints independently, by coupling sets of joints into adaptive functional groups.285
Although the definition of synergies (Bruton & O’Dwyer, 2018) vary in the literature, dimension-286 ality reduction is generally accepted as a signature of synergistic control (Todorov & Ghahramani,287 2004; Todorov, 2004). Consequently, to test this hypothesis, in Fig. 9 we plot the stable rank of288 the attention matrix. For attention matrix Al ∈ Rm×m for layer l, the stable rank is defined as:289 sr(Al) = ‖Al‖2F ‖Al‖2 = ∑ σ2i σ2max , where σi are the singular values of Al. We note that sr(Al) is small290 and oscillates between two values which correspond to attention maps where groups of limbs are291 activated simultaneously (denoted by dark columns), a characteristic signature of motor synergies.292 Hence, MetaMorph simplifies the control problem by learning to activate different motor synergies293 depending on both skm and s k p .294
6 CONCLUSION295
In this work, we explored how we can learn a universal controller for a large modular robot design296 space. To this end, we proposed MetaMorph, a Transformer approach based on the insight that robot297 morphology is just another modality on which we can condition the output of a Transformer. We298 showcased that pre-training on a large collection of diverse morphologies leads to policies which can299 generalize to unseen variations in kinematics, dynamics, new morphologies, and tasks. We hope that300 our work serves as a step towards realizing the potential of large-scale pre-training and fine-tuning301 in the field of robotics, a paradigm that has seen tremendous success in vision and language.302
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A IMPLEMENTATION DETAILS427
In this section, we provide additional implementation details for our method.428
A.1 INPUT OBSERVATIONS429
In our setup, at each time step t (we drop the time subscript for brevity) the robot receives an430 observation sk = (skm, s k p, s k g) which is composed of the morphology representation (s k m), the pro-431
prioceptive states (skp) and additional global sensory information (s k g ). We note that prior work432 (Kurin et al., 2021; Huang et al., 2020) has defined morphology as the connectivity of the limbs.433 However, connectivity is only one aspect of morphology, and in general a substantial amount of434 additional information may be required to adequately describe the morphology. Consider a modular435 robot composed of n ∈ 1...Nk modules. For each module skmi consists of: (1) Module Parame-436 ters: Module shape parameters (e.g. radius, height), material information (e.g. density), and local437 geometric orientation and position of the child module with respect to the parent module. (2) Joint438 Parameters: This consists of information about joint type (e.g. hinge) and its properties (e.g. joint439 range and axis), and actuator type (e.g. motor) and its properties (e.g. gear). All this information can440 be found for example, in the Universal Robot Description Format (URDF) or in simulator specific441 kinematic trees (e.g. MuJoCo XML (Todorov et al., 2012)).442
Similarly for each module skpi consists of the instantaneous state of the system: (1) Module Pro-443 prioception: 3D Cartesian position, 4D quaternion orientation, 3D linear velocity, and 3D angular444 velocity. (2) Joint Proprioception: Position and velocity in the generalized coordinates. Except the445 root node, each module will be connected to its parent module via a set of joints. We adopt the con-446 vention of providing joint parameters and proprioception information to the child node. We note that447 the importance of proprioceptive observations has also been observed in learning good locomotion448 (Lee et al., 2020; Kumar et al., 2021) and manipulation policies (Mandlekar et al., 2021).449
In general, additional global sensory information (skg ) can be camera or depth sensor images. To450 save computation, we provide the information about the terrain as 2D heightmap sampled on a non-451 uniform grid. The grid is created by decreasing the sampling density as the distance from the root of452 the body increases. All heights are expressed relative to the height of the ground immediately under453 the root of the agent. The sampling points range from 1m behind the agent to 4m ahead of it along454 the direction of motion, as well as 4m to the left and right.455
A.2 ENVIRONMENTS456
All our training and testing environments are implemented in MuJoCo (Todorov et al., 2012). For457 a detailed description about the environments and reward functions please refer Gupta et al. (2021).458 Gupta et al. (2021) evolved UNIMALS in 3 different environments: flat terrain, variable terrain459 and manipulation in variable terrain. For each environment at the end of evolution, they had 100460 task optimized robots. Out of these 300, we choose a subset of 100 UNIMALS as our train set. We461 ensure that no robot has the exact same kinematic tree or kinematic parameters. Similarly, we created462 a test set of 100 UNIMALS. Finally, for zero shot evaluation experiments we created the variants as463 described in § 5.3. For creating kinematic variations for joint angles, we randomly selected a joint464 range for each joint from Table 2 which had alteast 50% overlap with the original joint angle range.465 This helped in preventing robot variants which could not be trained.466
A.3 TRAINING HYPERPARAMETERS467
We use Transformers (Vaswani et al., 2017) to parametrize both policy and critic networks. Global468 sensory information is encoded using a 2-layer MLP with hidden dimensions [64, 64]. We use469 Proximal Policy Optimization (Schulman et al., 2017) for joint training of agents in all environments.470 All hyperparameters for Transformer and PPO are listed in Table 1. In addition, we use dynamic471 replay buffer sampling with α = 0.1 and β = 1.0. For all our ablations and baselines, we use the472 same hyperparameters. For MLP baseline we use a 2-layer MLP with hidden dimensions [64, 64]473 and for GNN baseline we use a 5-layer MLP with hidden dimension 512. The details of the joint474 training of modular robots are shown in Algorithm 1. We ensure that all baselines and ablations475 have approximately the same number of parameters (∼ 3.3 Million).476
A.4 EVALUATION METHODOLOGY477
In general, the performance of RL algorithms is known to be strongly dependent on the choice of478 seed for random number generators (Henderson et al., 2018). Hence, we run all our baseline and479 ablation experiments for 3 random seeds. We found that the training curves were robust to the choice480 of seeds as indicated by small standard deviation (Fig. 4, 10). Consequently, we evaluate the zero481 shot performance using pre-trained model corresponding to a single seed (Fig. 6). For our transfer482 learning experiments (Fig. 7, 8), we fine tune the model with the random seed corresponding to the483 one used for pre-training (for all 3 seeds).484
B ADDITIONAL EXPERIMENTS485
B.1 BASELINES AND ABLATIONS486
Baselines: We compare against the following baselines:487
(1) MetaMorph-AO: Direct comparison to Amorpheus (Kurin et al., 2021) is not feasible due to the488 following reasons: (1) does not work with 3D morphologies with variable number of joints between489 limbs; (2) does not incorporate exteroceptive observations; (3) Amorpheus ensures a balanced col-490 lection of experience by sequentially collecting data on each morphology and maintaining a separate491 replay buffer per morphology. Further, updates to policy parameters are also performed sequentially.492 This sequential nature of the algorithm is not amenable to scaling, and would require ∼ 30 GPU493 days to train for 100 million iterations on Nvidia RTX 2080, while our method only needs 1.5 GPU494 days (∼ 20x training speedup).495 Hence, we compare with MetaMorph-AO (Amorpheus Observation) as the closest variant to Amor-496 pheus, where we provide the same input observations as described in Kurin et al. (2021). Specifi-497 cally, we provide the following input observations: one hot encoding of unique limb ID, 3D Carte-498 sian position, 4D quaternion orientation, 3D linear velocity, and 3D angular velocity, position in499 generalized coordinates, and normalized joint angle ranges. Note that although both MetaMorph500 and Amorpheus don’t explicitly incorporate the graph structure as input to the policy, our input ob-501 servations include additional morphology information (see § B.1). In contrast, except for joint angle502 ranges, Amorpheus does not incorporate morphology information.503
(2) Multi-Task MLP: We train a 6-504 layer MLP with a hidden dimension505 of 512. The MLP receives the same506 observations as MetaMorph, and has507 the same number of parameters.508
Ablations: We perform additional509 ablation studies to understand the im-510 portance of learnt position embed-511 dings and morphology information512 in input observation. We refer our513 full method as MetaMorph and con-514 sider the following additional ab-515 lations: (1) MetaMorph-NMT (No516 Morphology information + Task en-517 coding): we only provide sk =518 (skp, s k g) as inputs, i.e. the model does519 not have access to information about520
the robot morphology. In addition, we provide a context token with binary encoding to iden-521 tify the morphology; (2) MetaMorph-HPE (Hand-designed Position Encoding): we provide sk =522 (skm, s k p, s k g) as inputs, with s k m containing a per limb unique ID.523
Fig. 10 shows the learning curves for joint training in the flat terrain environment for 100 training524 morphologies. Multi-task MLP baseline struggles to learn a good policy, which we attribute to the525 difficulty of the task due to the rich diversity of morphologies. We next investigate the role of learnt526 position embeddings and morphological information. MetaMorph-HPE performs slightly better than527 MetaMorph-NPE, indicating that adding unique limb ids improves performance. However, there is528
still a large gap between MetaMorph and MetaMorph-HPE, suggesting that learnt position embed-529 dings capture additional information. Although MetaMorph-AO performs better than MetaMorph-530 NM due to the addition of unique limb ids and joint angle ranges, it is still significantly worse than531 MetaMorph as it does not incorporate morphological information. Finally, MetaMorph-NMT also532 performs poorly due to the lack of morphological information.533
B.2 POSITION EMBEDDING534
To better understand the information captured by position embeddings we visualize their cosine535 similarity. We found that across all seeds and in all environments (we only show flat terrain for536 brevity) a diagonal pattern emerged (Fig 11). Although, this might suggest that learnt position537 embeddings are capturing a unique position identifier, we found that manually specifying a unique538 limb identifier to perform much worse (see MetaMorph-HPE in Fig. 10). Thus, indicating that these539 embeddings are capturing additional useful information. We believe that further investigation is540 needed to better understand the information captured by position embeddings and defer it to future541 work.542
B.3 KINEMATIC TREE TRAVERSAL ORDER543
We first process an arbitrary robot by creating a 1D sequence of tokens corresponding to the depth544 first traversal of its kinematic tree. We note that the DFS ordering is not unique, and for nodes545 with similar depth, we adopted the convention of visiting the nodes which come first in the MuJoCo546 XML. This convention is also adopted by MuJoCo when parsing the XML. We found that zero-shot547 transfer of the learnt policy was not robust to an opposite ordering of nodes and the performance548 dropped by ∼ 75%. However, we found that the policy could be made robust to variations in the549 DFS ordering by training the model with a simple data augmentation strategy of randomizing the550 visitation order of nodes at the same depth.551
C LIMITATIONS AND FUTURE WORK552
In this work, we explored how we can learn a universal controller for a large modular robot design553 space. We make a key assumption that we already had access to a large collection of robots which554 were optimized for the task of locomotion. Hence, currently we require a two stage pipeline where555 we first create robots and then pre-train them jointly. An important direction of future work would be556 to combine these two phases in an efficient manner. Moreover, although we found that MetaMorph557 has strong zero-shot generalization to unseen variations in kinematics and dynamics, there is indeed558 a performance drop on zero-shot generalization to new morphologies. Hence, an important avenue559 of exploration is creating algorithms for sample efficient transfer learning. Finally, our current suite560 of tasks focuses primarily on locomotion skills. An important line of future work will involve561 designing general purpose controllers which could perform multiple skills.562
Algorithm 1 MetaMorph: Joint Training of Modular Robots 1: Input: πθ: policy function Vφ: value function R: replay buffer K: training pool of robots Pk: probability of sampling robot k
2: Initialize: Learnable parameters θ for πθ, φ for Vφ, P uniform distribution 3: for i=1,2,..Niter do 4: # Collect robot experience in parallel 5: for j=1,2,..Nworkers do 6: Sample a robot k ∈ K, according to Equation 5 7: # Collect one episode of experience 8: τj ≡ {st, at, st+1, rt+1}j ∼ πθ 9: # Add data to the buffer
10: R← R ∪ τj 11: end for 12: # Update policy and value functions 13: for j=1,2,..Nepochs do 14: Sample a minibatch of data r from R 15: πθ ← PPOUpdate(πθ, r) 16: Vφ← PPOUpdate(Vφ, r) 17: end for 18: # Sample all robots uniformly at random initially 19: if i >= Nwarmup then 20: P ← samplingProbUpdate(R), according to Equation 5 21: end if 22: end for

Review summaries:
This paper presents MetaMorph, a Transformer based universal controller to learn behaviors across different robot morphologies. Their Transformer module takes a sequence of tokens as input, corresponding to the number of modules in the robot. Each input token comprises the proprioceptive and morphology information for each constituent robot’s module. MetaMorph is trained using a standard model-free Proximal Policy Optimization (PPO) method. However, to ensure all robots get to explore given environments and gather a similar amount of experience, the paper introduces a dynamic replay buffer which prioritizes robots’ selection for experience collection based on their previous iteration's episode lengths. The results are compared against MLP based experts trained for all individual robots, Graph Neural Network-based universal controller, and ablated modules of MetaMorph to highlight design choices. Moreover, results also demonstrate the generalization capacity of MetaMorph to different robots through zero-shot transfer and fine-tuning.  

This paper proposes a Transformer-based universal policy to control modular robots. Being the space of modular robots combinatorial, it is (almost) impossible to train specific policy for each sample of the "robot distribution". Therefore, the paper proposes a transformer-based architecture design with explicit information about the robot morphology that, trained on a large number of possible morphologies. This universal controller generalizes zero-shot to unseen robot designs belonging to the training distribution and can be efficiently fine-tuned on new robots and tasks. The approach is thoroughly evaluated on different tasks.