A Multi-Dimensional Pedagogy for Racial Justice in Writing Centers

In light of disciplinary conversations and increased attention to anti-racism in writing centers, we see a disciplinary mandate for writing centers to better articulate a pedagogy for racial justice that informs our everyday work, including but not limited to tutoring practice. This mandate, we believe, involves asking: How do we make actionable our commitment to racial justice when working with writers one-with-one? What interactional stances and pedagogical moves enact a pedagogy of anti-racism in writing centers? How do we prepare ourselves to enact this pedagogy? Our answers to these questions center around (1) articulating and frequently re-articulating our commitments to racial and social justice and (2) making these commitments actionable through both reflective self-work and action-oriented work-with-others, as we have written in the related article “Making Commitments to Racial Justice Actionable.” This work leads us to argue that a pedagogy of anti-racism must be more than a statement that we abhor racial injustice. Rather, this pedagogy must be multi-dimensional and include a positive and actionable articulation of the “ought to be” that we are aiming toward

CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments

In this paper we study a new reinforcement learning setting where the environment is non-rewarding, contains several possibly related objects of various controllability, and where an apt agent Bob acts independently, with non-observable intentions. We argue that this setting defines a realistic scenario and we present a generic discrete-state discrete-action model of such environments. To learn in this environment, we propose an unsupervised reinforcement learning agent called CLIC for Curriculum Learning and Imitation for Control. CLIC learns to control individual objects in its environment, and imitates Bob's interactions with these objects. It selects objects to focus on when training and imitating by maximizing its learning progress. We show that CLIC is an effective baseline in our new setting. It can effectively observe Bob to gain control of objects faster, even if Bob is not explicitly teaching. It can also follow Bob when he acts as a mentor and provides ordered demonstrations. Finally, when Bob controls objects that the agent cannot, or in presence of a hierarchy between objects in the environment, we show that CLIC ignores non-reproducible and already mastered interactions with objects, resulting in a greater benefit from imitation

Latent Plans for Task-Agnostic Offline Reinforcement Learning

Everyday tasks of long-horizon and comprising a sequence of multiple implicit subtasks still impose a major challenge in offline robot control. While a number of prior methods aimed to address this setting with variants of imitation and offline reinforcement learning, the learned behavior is typically narrow and often struggles to reach configurable long-horizon goals. As both paradigms have complementary strengths and weaknesses, we propose a novel hierarchical approach that combines the strengths of both methods to learn task-agnostic long-horizon policies from high-dimensional camera observations. Concretely, we combine a low-level policy that learns latent skills via imitation learning and a high-level policy learned from offline reinforcement learning for skill-chaining the latent behavior priors. Experiments in various simulated and real robot control tasks show that our formulation enables producing previously unseen combinations of skills to reach temporally extended goals by "stitching" together latent skills through goal chaining with an order-of-magnitude improvement in performance upon state-of-the-art baselines. We even learn one multi-task visuomotor policy for 25 distinct manipulation tasks in the real world which outperforms both imitation learning and offline reinforcement learning techniques.Comment: CoRL 2022. Project website: http://tacorl.cs.uni-freiburg.de