The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning. In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and propose a particular instantiation of such a system. Subsequently, we investigate a number of challenges of learning without instrumentation -including the lack of episodic resets, state estimation, and hand-engineered rewards -and propose simple, scalable solutions to these challenges. We demonstrate the efficacy of our proposed system on dexterous robotic manipulation tasks in simulation and the real world, and also provide an insightful analysis and ablation study of the challenges associated with this learning paradigm.
The paper takes seriously the question of having a robotic system learning continuously without manual reset nor state or reward engineering. The authors propose a first approach using vison-based SAC, shown visual goals and VICE, and show that it does not provide a satisfactory solution. Then they add a random pertubation controller which brings the robot or simulated system away from the goal and a VAE to encode a compressed state, and show that it works better.
The paper is a nice read, it contains useful messages thus I'm slightly in favor of accepting it, but I may easily change my mind as it suffers from serious weaknesses.
First, and most importantly, the experimental study is very short, the authors have chosen to spend much more space on careful writing of the problem they are investigating.
To mention a few experimental weaknesses, in Section 6.2 the authors could have performed much more detailed ablation studies and stress in more details the impact of using the VAE alone versus using the random pertubation controller alone, they could say more about the goals they show to the system, etc. There is some information in Figure 7, but this information is not exploited in a detailed way. Furthermore, Figure 7 is far to small, it is hard to say from the legend which system is which.
About Fig.8, we just have a qualitative description, the authors claim that without instrumenting they cannot provide a quantitative study, which I don't find convincing: you may instrument for the sake of science (to measure the value of what you are doing, even if the real-world system won't use this instrumentation).
So the authors have chosen to spend more space on the positionning than on the empirical study, which may speak in favor of sending this paper to a journal or magazine rather than a technical conference. But there is an issue about the positionning too: the authors fail to mention a huge body of literature trying to address very close or just similar questions. Namely, their concern is one the central leitmotives of Developmental Robotics and some of its "subfields", such as Lifelong learning, Open-ended learning, Continual learning etc.  The merit of the paper in this respect is to focus on a specific question and provide concrete results on this question, but this work should be positionned with respect to the broader approaches mentioned above. The authors will easily find plenty of references in these domains, I don't want to give my favorite selection here.
Knowing more about the literature mentioned above, the authors could reconsider their framework from a multitask learning perspective: instead of a random perturbation controller, the agent may learn various controllers to bring the system into various goal states (using e.g. goal-conditioned policies), and switching from goal to goal to prevent the system fro keeping stuck close to some goal.
More local points:
In the middle of page 5, it is said that the system does not learn properly just because it is stuck at the goal. This information comes late, and makes the global message weaker.
in Fig. 4, I would like to know what is the threshold for success.