Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics

• URL: http://arxiv.org/abs/2304.12289v1
• Date: Mon, 24 Apr 2023 17:35:47 GMT
• Title: Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics
• Authors: Kuo-Hao Zeng, Luca Weihs, Roozbeh Mottaghi, Ali Farhadi
• Abstract summary: We propose to model the impact of actions on-the-fly using latent embeddings. By combining these latent action embeddings with a novel, transformer-based, policy head, we design an Action Adaptive Policy. We show that our AAP is highly performant even when faced, at inference-time with missing actions and, previously unseen, perturbed action space.
• Score: 57.671493865825255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A common assumption when training embodied agents is that the impact of taking an action is stable; for instance, executing the "move ahead" action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise. This assumption is limiting; an agent may encounter settings that dramatically alter the impact of actions: a move ahead action on a wet floor may send the agent twice as far as it expects and using the same action with a broken wheel might transform the expected translation into a rotation. Instead of relying that the impact of an action stably reflects its pre-defined semantic meaning, we propose to model the impact of actions on-the-fly using latent embeddings. By combining these latent action embeddings with a novel, transformer-based, policy head, we design an Action Adaptive Policy (AAP). We evaluate our AAP on two challenging visual navigation tasks in the AI2-THOR and Habitat environments and show that our AAP is highly performant even when faced, at inference-time with missing actions and, previously unseen, perturbed action space. Moreover, we observe significant improvement in robustness against these actions when evaluating in real-world scenarios.

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