  A crucial design decision for any robot learning pipeline is the choice of
policy representation: what type of model should be used to generate the next
set of robot actions? Owing to the inherent multi-modal nature of many robotic
tasks, combined with the recent successes in generative modeling, researchers
have turned to state-of-the-art probabilistic models such as diffusion models
for policy representation. In this work, we revisit the choice of energy-based
models (EBM) as a policy class. We show that the prevailing folklore -- that
energy models in high dimensional continuous spaces are impractical to train --
is false. We develop a practical training objective and algorithm for energy
models which combines several key ingredients: (i) ranking noise contrastive
estimation (R-NCE), (ii) learnable negative samplers, and (iii) non-adversarial
joint training. We prove that our proposed objective function is asymptotically
consistent and quantify its limiting variance. On the other hand, we show that
the Implicit Behavior Cloning (IBC) objective is actually biased even at the
population level, providing a mathematical explanation for the poor performance
of IBC trained energy policies in several independent follow-up works. We
further extend our algorithm to learn a continuous stochastic process that
bridges noise and data, modeling this process with a family of EBMs indexed by
scale variable. In doing so, we demonstrate that the core idea behind recent
progress in generative modeling is actually compatible with EBMs. Altogether,
our proposed training algorithms enable us to train energy-based models as
policies which compete with -- and even outperform -- diffusion models and
other state-of-the-art approaches in several challenging multi-modal
benchmarks: obstacle avoidance path planning and contact-rich block pushing.
